site selection for commercial biofuel production …686082/s...site selection for commercial biofuel...

Site selection for commercial biofuel production from algae and

sugarcane, using GIS modelling in Queensland, Australia

Masoumeh Sedghamiz (BSc)

A thesis submitted for the degree of Master of Philosophy at

The University of Queensland in 2017

School of Geography, Planning and Environmental Management

II

Abstract

A number of factors including, growing energy consumption and increasing fossil

fuel price along with greenhouse gas emission concerns, have increased global attention

to biofuel as a potential sustainable energy product. Even with a transition to electricity-

driven transport (e.g. electric cars), high energy-density fuels will still be required for larger

vehicles (planes, boats, trucks) and communities that are not connected to the electricity

grid. While environmental benefits of biofuel have been considered in promoting the

industry, existing first generation biofuel crops compete with agricultural land or biodiverse

natural landscapes. Nevertheless, biofuel (in particular second and third generation

biofuel) could be an economic and self-reliant regional energy product. Additionally, it

would increase the level of services, quality of life and creation of employment, etc. for

rural areas. In Australia, the total primary energy consumption is projected to grow by

nearly 42 per cent until 2049-50, therefore the demand for energy is expected to continue

to mount. Hence it is critical to investigate cost-effective investment and sustainability in

Australia’s energy future.

The Australian state of Queensland with suitable broad land and climate, is generally

well suited to biofuel production. Among various types of biofuel resources, microalgae

are considered as one of the best feedstocks for sustainable biofuel generation, as they

can be grown on non-arable land with nearly any source of water (fresh, brackish or saline)

and CO2. Queensland has vast land resources with an area of 1,730,648 square

kilometres and the total length of Queensland's mainland coastline is 6,973 km (4,333 mi)

and the appropriate climate needed to produce algae as an alternative viable source of

fuel.

Although QLD has vast land resources and suitable climatic condition for algae

cultivation, there is a need to allocate the suitable sites according to climatic and

environmental constraints and economic limitations such as land value, for sustainable

and economic production of biofuel. For a cost-effective production, land value,

biophysical parameters and access to resources and roads are critical criteria to consider

for locating an algae farm. Therefore, the location decision is a priority to financial success

in the biofuel industry.

III

In this case study, optimal commercial-scale biofuel production sites in Queensland

were identified using a Multi-Criteria Analysis (MCA) tool, and a staged Geographical

information System (GIS) analysis. In this study, the low-value regions with land use

consideration were identified and proximity to roads mapped by Euclidean distance.

In another stage, they were combined with eco-climatic maps and ranked by their

importance. Eventually, the MCA tool was employed to map the optimal locations. The

outcomes of this study advance the techniques for biofuel site assessment and provide

comprehensive and accurate results which can support the microalgae-based biofuel

industry development in Queensland and evolve better management strategies for

sustainable land use planning in the state. Two maps that resulted from these analyses

are climate suitability and overall algae farming suitability. The first map shows the site

suitability for commercial microalgae farms with biofuel production as their primary

purpose, according to eco-climatic and land use criteria. The overall algae suitability

shows the spatial distribution of microalgae production suitability levels.

Another part of this study includes the investigation for potential sites for cultivating

both sugarcane and microalgae. The aim was to find out if algae could be produced in

sugarcane production sites, in case of land use change considerations in the future. The

GIS overlay technique has been adopted and the algae suitability map from Chapter 2

was overlayed on the sugarcane potential site map produced by Audit. Also, proximity to

roads and CO2 resources were added to the analysis. The map result shows that, there is

a lot of potential for cultivation both plants in north-western and eastern regions of

Queensland with low value land.

IV

Declaration by author

This thesis is composed of my original work, and contains no material previously

published or written by another person except where due reference has been made in the

text. I have clearly stated the contribution by others to jointly-authored works that I have

included in my thesis.

I have clearly stated the contribution of others to my thesis as a whole, including

statistical assistance, survey design, data analysis, significant technical procedures,

professional editorial advice, and any other original research work used or reported in my

thesis. The content of my thesis is the result of work I have carried out since the

commencement of my research higher degree candidature and does not include a

substantial part of work that has been submitted to qualify for the award of any other

degree or diploma in any university or other tertiary institution. I have clearly stated which

parts of my thesis, if any, have been submitted to qualify for another award.

I acknowledge that an electronic copy of my thesis must be lodged with the University

Library and, subject to the policy and procedures of The University of Queensland, the

thesis be made available for research and study in accordance with the Copyright Act

1968 unless a period of embargo has been approved by the Dean of the Graduate School.

I acknowledge that copyright of all material contained in my thesis resides with the

copyright holder(s) of that material. Where appropriate I have obtained copyright

permission from the copyright holder to reproduce material in this thesis.

V

Publications during candidature

No publications.

Publications included in this thesis

No publications included.

Contributions by others to the thesis

No contributions by others.

Statement of parts of the thesis submitted to qualify for the award of another degree

None.

VI

Acknowledgements

This thesis could not have been completed without the assistance, advice and

support of a number of people. Firstly, I’d like to thank my advisory team, Dr. David Pullar

and Prof. Peer Schenk, for their support and advice throughout my postgraduate study.

Staff at the School of Geography, Planning and Environmental Management

provided administrative and technical support throughout my studies, particularly Judith

Margaret Nankiville.

I’d like to say a very big thank you to my friends and family for putting up with me

and providing encouragement and moral support when I most needed it. Finally, a big

thank to my husband, Mohammad for his unconditional love and support whose presence

made a world of difference for me!

VII

Keywords

Energy resources, GIS, MCA, biofuel, renewable energy, sustainable, eco-climatic,

microalgae, land use, evaluation

Australian and New Zealand Standard Research Classifications (ANZSRC)

050205 Environmental Management (50%),

070108 Sustainable Agricultural Development (35%), 090608 Renewable Power and Energy Systems Engineering (15%)

Fields of Research (FoR) Classification

FoR code: 0502, Environmental Science and Management, 40%

FoR code: 0803, Computer Software, 60%

VIII

Table of Content

CHAPTER 1. Introduction 1.1 Background ............................................................................................................................ 2 1.2 Thesis aim and Objectives and Questions .............................................................................. 6 1.3 Literature Review ................................................................................................................... 7

1.3.1 Challenges in Use/Demand for Energy Resource ........................................................ 8 1.3.2 Renewable/Biofuel Energy .......................................................................................... 9 1.3.3 ArcGIS evaluation methods ...................................................................................... 12

1.3.3.1 Overview of MCA Approaches............................................................................. 13 1.3.3.2 Overview of Biofuel site selection studies ............................................................ 16 1.4 Approach ........................................................................................................................... 22 1.5 Thesis outline ..................................................................................................................... 23

CHAPTER 2. Site selection for commercial microalgae cultivation using Multicriteria GIS modelling in Queensland, Australia

2.1 Introduction ........................................................................................................................... 25 2.2 Study area and Materials ...................................................................................................... 29

2.2.1 Study area ................................................................................................................... 29 2.2.2 Algae site suitability ..................................................................................................... 32 2.2.3 Resource evaluation for biofuel production scale up ................................................... 33

2.2.3.1 Climatic variables .................................................................................................... 34 2.2.3.2 Land use variables ................................................................................................. 34 2.2.3.3 Economic variables ................................................................................................. 35

2.2.4 Data sources ................................................................................................................. 36

2.3 Methodology ......................................................................................................................... 38

2.3.1 Suitability analysis ....................................................................................................... 38 2.3.1.1 Overview of multi-criteria analysis ............................................................................. 38 2.3.2 Reclassification and suitability analysis ........................................................................ 40

2.4 Results…………………………………………………………………………………………………..41 2.4.1 Eco-climatic map ......................................................................................................... 43 2.4.2 Algae production suitability map .................................................................................. 36

2.5 Discussion ............................................................................................................................. 47 2.6 Conclusion ............................................................................................................................ 48

CHAPTER 3. Comparison of potential sites for microalgae and sugarcane as biofuel crops

3.1 Introduction ........................................................................................................................... 50 3.1.1 Sugarcane industry in Qld ........................................................................................... 50 3.1.2 Yes or no to continue sugarcane production ............................................................... 53

3.2 Methods ................................................................................................................................ 55 3.3 Results .................................................................................................................................. 59 3.4 Discussion ............................................................................................................................. 62

3.4.1 Cost effectiveness comparison of producing biofuel from algae and sugarcane .......... 64 3.5 conclusion ............................................................................................................................. 67

CHAPTER 4. Synthesis and Conclusion 4.1 overview ................................................................................................................................ 69 4.2 The contribution of biofuel production ................................................................................... 69 4.3 Limitation and future research ............................................................................................... 70

4.4 Conclusion ............................................................................................................................ 70

List of References ..................................................................................................................... 72

IX

List of Figures & Tables

Figure 1.1: The highest greenhouse gas emitter countries per capita 2010 .......................... 2

Figure 1.2: Percentage change in emissions by sector, Australia, 1989-90 to 2012-13 ......... 3

Figure 1.3: World annual fuel ethanol production, 1975-2009 ............................................... 4

Figure2.1: Study area-Queensland government boundaries- Major climate classes and the average rain fall ................................................................................................................. 30

Figure 2.2: Queensland bio-industries map ........................................................................ 31

Figure 2.3: Essential factors for identifying optimal sites ..................................................... 33

Figure 2.4: Radar plot of criteria used in eco-climatic MCA modelling ................................ 42

Figure 2.5: suitability map according to eco-climatic and land use criteria ......................... 43

Figure 2.6: Radar plot of criteria used in algae site selection MCA modelling ..................... 45

Figure 2.7: Spatial distribution of microalgae production suitability levels .......................... 46

Figure 3.1: Sugarcane worldwide distribution. ..................................................................... 51

Figure 3.2: Queensland Sugarcane production regions and gross value ................... ……..52

Figure 3.3: Percentage of current sugarcane land in each region ...................................... 53

Figure 3.4: Harvested sugar cane area and tonnage ......................................................... 54

Figure 3.5: Queensland Sugarcane potential production sites ............................................ 57

Figure 3.6: The process of the final map production ............................................................ 58

Figure 3.7: Queensland Algae and Sugarcane suitable production sites. ............................ 58

Figure 3.8: Queensland Algae and Sugarcane suitable production sites close to the mills with economically land value .............................................................................................. 61

Figure 3.9: Greenhouse gas emissions for cane sugar, showing contributing activities in Queensland………………………………………………………………………………………….63

Figure 3.10: Technoeconomic analysis for microalgal oil with feedstock production. Shown are operating (OPEX) and capital (CAPEX) costs for a 10 ha farm with utilising purchased CO2……………………………………………………………………………………………………66

Figure 3.11: Technoeconomic analysis for microalgal oil with feedstock production. Shown are operating (OPEX) and capital (CAPEX) costs for a 10 ha farm without utilising purchased CO2 ………………………… ............................................................................................... . 66

Table 1.1: An overview of MCA approaches studies .......................................................... 15

Table 1.2: Biofuel Site Selection Studies ............................................................................. 21

Table 1.3: Requirement variables and data types .............................................................. 22

Table 1.4. Schematic overview of thesis structure; each boxes indicate chapters comprised of research articles which individually address the main research questions of the study .. 23

Table2.1: Variables, data sources and type, ideal condition of criteria used in study ......... 37

Table 2.2: Frequencies, importance ranking and weight criteria calculated, using Meta – Analysis……………………………………………………………………………………..............40

Table 2.3: Variables and Influence weighting use in Radar plot of Eco-Climate suitability map…………………………………………………………………………………………………..42

Table 2.4: Radar plot of algae site selection MCA modelling ............................................... 44

Table 3.1: Sugarcane production in major countries (by area harvest in 2008), according to FAO estimates ................................................................................................................. 51

Table 3.2: Limitation criteria used for assessing agricultural land suitability in Queensland..56

Table 3.3: Selected area suitable for sugarcane and algae farm………………………………61

X

List of Abbreviations used in the thesis

BOM: Bureau of Meteorology AUDIT: The Queensland Agricultural Land Audit (the Audit) identifies land important to current and potential future agricultural production across Queensland. It aims to help Queensland better plan for future food and fiber production.

ArcGIS: Geographical Information System ABARES: the Australian Bureau of Agricultural and Resource Economics and Sciences, the science and economics research division of the Department of Agriculture and Water Resources. BREE: Bureau of Resources and Energy Economics GGE: Greenhouse gas emission

MCA: Multi-criteria analysis

Chapter1. Introduction

The University of Queensland | School of Geography, Planning and Environmental Management

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1.1 Background

Over the past decade, environmental issues such as, global warming along with

increasing vulnerability due to oil dependencies, have contributed to the urgent need to

research and discover alternative sustainable energy sources.

Australia was the most greenhouse gas emitter country in the world in 2010 and its

emissions have increased 30.5% since 1990 (Fig 1.1). The main reasons for the

increase in Australia’s emissions are stationary energy which includes emissions from

direct combustion of fuels, predominantly in the manufacturing, mining, residential and

commercial sectors – up by 43% and Transport emissions – up by 53.6% (Environment,

2013)(Fig 1.2). Rising concern about climate change and its necessary mitigation as

well as the increasing awareness of the relationship between climate change and

sustainability has driven notice to a search for a secure and clean source of energy.

Biofuels have been put forward as one of a range of alternatives with lower emissions

and a higher degree of fuel security (O'Connell et al., 2007; Seabra et al., 2011).

Figure 1.1: The highest greenhouse gas emitting countries per capita 2010


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Figure 1.2: Percentage change in emissions by sector, Australia, 1989-90 to 2012-13. Source: Department of the Environment estimates.

Liquid biofuels have recently attracted increased attention in Australia – as in other

countries all over the world - as a major alternative to petroleum based transportation

fuels (Fig 1.3). Their major benefits are: i) it is a renewable fuel, ii) may be grown so it

is commonly available, iii) encourages regional development, iv) creates jobs in rural

manufacturing, v) reduces greenhouse gas emission, and vi)it is biodegradability

(Demirbas, 2009b; Keating and Carberry, 2010; Puri et al., 2012).

On the other hand, there may be negative impacts for biofuels: i) they compete

with other agricultural crops and put pressure on demand for land (Harvey and Pilgrim,

2011), and ii) a growing Biofuel industry will affect the supply of feed grain for livestock,

particularly in drought years and this will place upward pressure on the price of grain

(O'Connell et al., 2007).


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Figure 1.3: World annual fuel ethanol production, 1975-2009, source: www.earth-

policy.org Source: F.O. Licht, World Ethanol and Biofuels Report.

There are different types of plants for producing biofuels. Among them, sugarcane

is the most common crop for bioethanol; it has the advantage that it is already grown

in Queensland and is a multi-profit product. However, it places land use pressures on

agriculture as it requires land with fertile soils and high rainfall. Additionally, there are

restrictions on choosing potential areas for growing sugarcane such as: i) climate

requirements for minimum temperature and avoiding frost, and ii) economic

requirements in terms of distance to processing mills (Audit, 2013). While cane growing

provides direct economic benefits, environmental values are becoming increasingly

important and should be considered (Mallawaarachchi and Quiggin, 2001).

Another alternative crop for biofuel production, is algae with these significant

benefits: i) algae do not need to compete with valuable high intensity agricultural land

and they can be grown on marginal or non-agricultural land so avoiding adverse

impacts on land use and food prices (Gressel, 2008), ii) algae grow in fresh, brackish

or even saline water (Prasad et al., 2014), iii) they can have a reduced greenhouse gas

and energy footprint (Campbell et al., 2009).

0

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6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

1975 1980 1985 1990 1995 2000 2005 2010

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Recently, there has been a strong focus on reducing greenhouse gas emissions

from aviation fuel and on limiting carbon emissions. So, bio- derived jet fuels have

opportunities to take an essential part in eliminating those concerns. The aviation

industry in Australia has aspirations to supply 5% of its domestic fuel use from biomass

by 2020 (Graham et al., 2011; Murphy et al., 2015). Another advantage of microalgae

is, its oil content which is up to 20-50% and can be extracted and used for biodiesel. In

addition it can be refined to compounds that can replace jet fuel and is increasingly

being used in attempts to reduce the environmental impacts of aviation and to ensure

energy security in the industry (Fortier et al., 2014; Klein‐Marcuschamer et al., 2013b).

Although biofuel has a lot of advantages, but the production cost needs to be

compatible with petroleum based fuels. The ability to produce and integrate large

volumes of biofuels cost-effectively and sustainably are primary concerns of which

policy makers should be aware (Sims et al., 2011). But, there are a range of barriers to

perform large size cost-effective biofuel production as many factors involve in.

According to the statistics of an Audit report by The Queensland Department of

Agriculture, Forestry and Fisheries, Queensland has the potential to increase the land

use for sugarcane from 0.33 to 4.06 %. It means in this state there is an enormous

opportunity for growing sugarcane. Based on technology of using waste resources

needed for growing algae, they can also be generated by cane growing and sugar

processing (Prasad et al., 2014). Hence, it could be beneficial environmentally and

economically to identify the specific and suitable regions with favourable biophysical

and climatic condition for both sugarcane and microalgae.


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1.2 Thesis Aim, Objectives and Questions

In this study, Queensland was selected as the study area with huge

development opportunities for economical investment in commercial bioenergy

production, considering its suitable climatic and economic factors(Queensland

Goverment, 2017) (Picture 1.1). As an economical advantage for Queensland, algae

based biofuel production projects can make use of vast arable land across the state

that is naturally unsuitable for crop production. The expected large gap between future

demand and potential domestic supply in Queensland requires expanding biofuel

production in areas which have the land and the climate needed to produce raw

feedstocks on a large scale.

The aim of the current study is investigating the most suitable locations for

allocating biofuel farms for long term economic and environmental sustainability, before

any investment for large-scale biofuel production.

Picture 1.1: Study area: Queensland, Australia.


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The specific objectives of this research were to:

I. To evaluate availability of suitable lands for crops for bioethanol production

according to land uses, eco-climatic parameters.

II. Assessing the land use implications comparing sugarcane and algae for

bioethanol production according to economic efficiency and land use

concerns.

III. Updated map results for future sustainable land use planning in Qld.

To obtain the objectives of the study, the research questions below have been

investigated:

1. How do different criteria which influence algae production along with land-use

and socio-economic factors determine the production location of algae?

2. How can suitability modelling can be adopted to allocate a commercial

production site for microalgal biofuel?

3. How does sugarcane production affect the Qld environment and can algae be a

substitute energy crop to mitigate these effects?

4. Are there any locations which are now under sugarcane production and also

suitable for algae production that can be considered for land-use change

consideration?

1.3 Literature Review

A large body of literature exists on biofuel production in terms of theoretical,

technical, environmental, economics and implementation to support the basis for

development of government policy and/or industry investment (Brennan and Owende,

2010; Hu et al., 2008; Li et al., 2008; Lundquist et al., 2010).

The majority of these studies consider economic factors as the most common

criteria in their modelling, based on biomass type/resources and final market analysis.


8

Recently there is growing interest to examine how these opportunities vary across

space and ideal land use suitability allocation (Borowitzka et al., 2012; Coleman et al.,

2014; Das and Salam, 2014; Klise et al., 2011; Maxwell et al., 1985; Quinn et al., 2012).

The literature review consisted of results from search using following search

strings: biofuel and environment; energy and demand; renewable energy and economy;

spatial allocation and GIS. The following section discussed about the related studies

in: (1) Challenges in use/demand for energy resources issues; (2) renewable/biofuel

energy; (3) ArcGIS evaluation methods and its applications in environmental

management scope.

1.3.1 Challenges in Use/Demand for Energy Resources Issues

Demand for energy is expected to grow during the next few decades in

Australia (Geoscience Australia and BREE, 2014) and the energy consumption is

projected to increase by 63 per cent by 2029-30 (Commonwealth of Australia, 2007).

Australia’s combined dependency on crude oil and fuel imports for transport has grown

from around 60% in 2000 to over 90% today (Biofuels Association of Australia, 2014)

and it is projected to increase to 76% by 2030 (ABARE, 2010; Geoscience Australia

and BREE, 2014). On the demand side, for long term supply and price stability, there

is concern over whether Australia is resilient to future fuel security challenges.

Therefore, alternate fuels, particularly those that are potentially in plentiful supply in

Australia, are the obvious option to improving our fuel dependence on both the supply

and demand side (Blackburn, 2013).

From an environmental perspective, greenhouse gas emissions (GGE) in

Australia have grown by 24.7%, since 1990, which is mainly caused by the electricity

and transport sector (Department of Environment, 2015). Rising concerns about

climate change and increasing awareness of the relationship between climate change

and sustainability urged the Australian government to develop the Clean Energy Future

plan. The plan is directly aimed at mitigating the impacts of climate change by

ambitiously targeting to cut GGE by at least 5 per cent compared with 2000 levels by

2020 (Commonwealth of Australia, 2007; Commonwealth of Australia, 2011).


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Recent increases in demand for petroleum based transportation fuels (i.e. aviation)

and their GGE emissions encourage industry to support the development of drop-in

renewable fuels (Elmoraghy and Farag, 2012; Klein-Marcuschamer et al., 2013).

Biofuels have direct, fuel-cycle GGE emissions that are typically 30 – 90 % lower per

kilometre travelled than those for gasoline or diesel fuels (IPCC, 2014).

By taking appropriate action towards clean energy, Australia can look forward to

protecting environment and long term economic prosperity. Achieving that, the Federal

Government has recently established a ‘‘Clean Energy Finance Corporation’’ which will

invest AUD$10 billion in developing renewable energy, and low-pollution and energy

efficient technologies (Puri et al., 2012).

Al in all, the necessity to study new sources of clean energy, is undoubted a

way to address energy demands and mitigate GGE concerns. The supply sources

locations for alternative energy is also vital important to be considered and the sio-

economic factors are inevitable to be neglected.

1.3.2 Renewable/Biofuel Energy

Growing environmental and energy concerns have led to consideration of

alternative energy sources based on production of biofuel in Australia (Puri et al., 2012).

In Australia there are large scale opportunities available that appear to offer a range of

environmental and social benefits, in addition to commercial bioenergy (Stucley et al.,

2012). According to Ramachandra and Shruthi (2007), for regional energy supply

independence it is vital for countries to search for renewable, alternate and non-

polluting sources of energy. Biofuel production is also suited to rural and remote areas

with the potential of significantly promoting their economic and development. As long

as sustainability and reduction of greenhouse gas emission, biofuels offer the potential

to increase the level of services for rural population and creation of employment

(Demirbas, 2009a; Gheewala et al., 2011). Consequently, through the efficient use of

locally available bioenergy sources the quality of life in rural areas can be improved

(Ramachandra and Shruthi, 2007).


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Biofuel feedstocks divide into 4 broad categories: (1) high-efficiency feed stocks

(e.g. palm oil, sugar cane); (2) moderate- efficiency feedstocks (e.g. corn, soybean,

rapeseed, sugar beet); (3) feedstocks under development (e.g. sweet sorghum,

Jatropha); and (4) dedicated energy feedstocks (e.g. switchgrass, miscanthus, short

rotation crops, algae, waste) (Elbehri et al., 2013). According to O'Connell et al. (2007),

the key crops which are currently use for bioethanol production in Australia are

sugarcane, molasses, wheat, barley, maize and sorghum, while future potential biofuel

production based on Jatropha, Pongamia, Moringa, Hura crepitans and algae is under

research.

Microalgae in particular have gained wide attention as a potential source of

biofuel. This commodity suits non-arable lands, utilises virtually any source of water,

may uptake waste CO2 sources and produces many profitable by-products, alongside

co-benefits of GGE mitigation. Furthermore, microalgae are superior in productivity

compared with plant crops in land area requirement and water consumption for

cultivation feedstock. They are considered as a reliable and continuous supply of fuel

due to their high oil content and continual-harvest characteristics (Brune et al., 2009; Li

et al., 2008; Pate et al., 2011; Pittman et al., 2011; Schenk et al., 2008; Singh et al.,

2011). In commercial plants one of the following four technologies is typically used to

cultivate algae: 1. extensive ponds (lagoons); 2. raceway and circular ponds; 3. tubular

photo bioreactors; 4. fermenters (where algae are grown on organic substrates in the

dark). Among these systems, open ponds are the most widely used for commercial

large-scale outdoor microalgae cultivation (Borowitzka, 2013; Borowitzka et al., 2012;

Schenk et al., 2008).

In practice, to date, the lowest cost of commercially produced microalgal oil is still

much higher than the reasonable medium-term price target to become cost competitive

with petroleum diesel (Borowitzka et al., 2012; Stephens et al., 2010). One of the key

factors for a technically and economically viable biofuel resource is that, it should be

competitive and cost less than petroleum fuels (Brennan and Owende, 2010). The

challenges to reach that goal and the fundamental barriers to development of the

biofuel industry are water and nutrients availability, harvesting methods and high costs

of oil extraction, land value, land availability, facilities cost, existing land use, proximity


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to resources and infrastructures, climate requirements, government policies and

supports (Singh et al., 2011; Stephens et al., 2010).

While other energy sources are concentrated in a limited number of countries,

renewable energy resources can be produced over wide geographical areas. However,

there are many possible scenarios of bioenergy production, and the options vary with

geographical location (Davis et al., 2011). Moving towards large scale production of

biofuel, needs choosing the most proper crops and the suitable location according to

the eco-climatic and socio-economic considerations(Maxwell et al., 1985).

The major requirements of growing biofuel crops are water resources availability,

suitable temperature and slope (Borowitzka et al., 2012; Brennan and Owende, 2010;

Pate et al., 2011; Prasad et al., 2014; Quinn et al., 2012; Quinn et al., 2013; Wigmosta

et al., 2011). The lack of each one of those would be a significant spatial limitation for

allocating a biofuel farm. As Panichelli and Gnansounou (2008) indicated, biomass to

energy projects are highly geographically dependent and the plant’s profitability can be

strongly influenced by its location (Panichelli and Gnansounou, 2008). Notably,

resources for land, water and climate provide different regions with widely contrasted

agricultural potentials (Harvey and Pilgrim, 2011). Hence, it is preferable for biofuel

farm sites to be located in areas with suitable biophysical and climate characteristics.

These considerations are helpful for desirable economic biofuel project investment. In

addition the suitability of farming locations depends on other potential positive effects,

such as reduction of run-off, soil erosion and sedimentation in rivers and dams, together

with increased water retention (Gheewala et al., 2011).

However, biofuels industry could have some major shifts on agriculture, food

industry and notably on land use, depends on where it is located and the type of crop

(O'Connell et al., 2007). So, the other issue for expanding biofuel production is

focussing on land use competition with food (Goldemberg et al., 2008) and land value

pressure (Coggan et al., 2008), so the key questions are what land and where? It has

been suggested that, energy crops such as algae that can be grown on less productive

or marginal lands has the potential to lead to a marked reduction in competition for land

between energy and food over the coming decade (Harvey and Pilgrim, 2011).


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One of the land use implications for growing biofuel crops in marginal lands, is

increasing opportunities outside agriculture which leads to high land price (Strijker,

2005) and investment in other profitable industries like tourism. Ecological and

environmental issues are the other concerns in biofuel production in those areas (Cai

et al., 2010). Therefore, minimum risk to food security, loss and degradation of habitat,

biodiversity and other environmental damages are among the main aspects that will

determine the sustainability of the Biofuel project (Group and Management, 2009;

Nhantumbo and Salomão, 2010).

Although the scientific literature indicates very high potential productivity for

biofuel crops in laboratory, it is not certain if this could be achieved in practice on an

industrial scale. Hence the need to address the potential, available locations where

climate and biophysical parameters are suitable for commercial cultivation of biofuels

is vitally important. Considering the constrain factors, the investment of biofuel industry

requires precise investigation of allocation and optimal geographical locations for

potential biofuel farms.

Therefore, this specific study on regional environmental conditions for growing

biofuel crops is a fundamental for maximising the benefit of bioenergy production

regionally. The result would readily enable assessment of how much potential-suitable

land might be located in a region which leads towards better management of land use

as a core element of any biofuel investment project.

1.3.3 ArcGIS Evaluation Method

This section provides an overview of MCA approaches and an overview of biofuel

site selection studies. It contains summary of related studies in both topics which were

guidance for this research.


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1.3.3.1 Overview of MCA Approaches

Geographical information systems (GIS) been widely adopted in decision making

in land use allocation, site, and route selection problems, with the privilege of helping

the decision makers to assign priority weights to decision criteria, evaluate the suitable

alternatives, and visualize the results of choice (Carver, 1991; Malczewski and Rinner,

2015). GIS provides the decision-maker with a powerful set of tools for the manipulation

and analysis of spatial information. A method adopted for approaching many spatial

problems, such as site selection or land use allocation, which require the decision-

maker to consider multiple criteria in order to choose the best alternative, is Multi

Criteria Evaluation (MCE) method (Hajkowicz et al., 2000; Jankowski, 1995).

MCE is commonly achieved by Boolean overlay or Weighted Linear Combination

procedure. In the first method, all criteria are reduced to logical statements of suitability

and then combined by means of one or more logical operators such as intersection

(AND) and union (OR). While, in the second method continuous criteria (factors) are

standardized to a common numeric range, and then combined by means of a weighted

average (Eastman et al., 1998; Malczewski, 2004).

Multiple-criteria decision analysis (MCDA) is a family of techniques that aid

decision makers in formally structuring multi-faceted decisions and evaluating the

alternatives(Greene et al., 2011). Combining GIS and MCDA for land planning involves

many tasks including data gathering and structuring, and computation of criteria using

spatial analysis and simulation (Joerin et al., 2001). The main steps in Multi-criteria

analysis are criteria selection, determining criteria weights according to the relative

importance of criteria, the acceptable alternatives are ranked by MCDA methods with

criteria weights and finally, the alternatives’ ranking is ordered and the process is ended

(Wang et al., 2009).

Weighted overlay analysis is one of the effective techniques in MCE for land use

suitability mapping and analysis with multiple criteria based decision-making purpose.

Weighted overlay analysis is a component of spatial modelling using spatial multicriteria

evaluation, which assigns more importance to some criteria over others(Malczewski,

2004; Malczewski and Rinner, 2015).


14

In this analysis, the pixel (cell) of feature classes of a particular thematic layer is

assigned with numeric weight values to combine mathematically to produce a new

value to the corresponding pixels in the output layer. The weighted overlay analysis

applies a common scale values to the multiple thematic layers to produce an output

layer (Kaliraj et al., 2015).

To meet the objective, the multiple thematic layers have been analysed using an

algorithm of weighted overlay analysis in ArcGIS environment (Esri, 2011). This

technique was used for suitability analysis in this study for spatial multicriteria

evaluation.

Different variety of land suitability studies have been performed using multicriteria

evaluation approach (Charabi and Gastli, 2011; Garmendia and Gamboa, 2012;

Hajkowicz and Collins, 2007; Hajkowicz, 2008; Perpiña et al., 2013; Zhu et al., 2001).

A list of studies using multicriteria evaluation approach, GIS application are listed in

Table 1.1.

The University of Queensland | School of Geography, Planning and Environmental Management 15

Table 1.1: An overview of MCA approaches studies

Stefan Hajkowicz & Kerry Collins 2007

Definition for MCA Types of MCA Techniques Types of MCA Applications

A Review of Multiple Criteria Analysis for Water Resource Planning and Management

Eneko Garmendia, Gonzalo Gamboa

Determining Weights in social MCE Weighting social preferences in participatory multi-criteria evaluations

Stefan A. Hajkowicz , Geoff T. McDonald & Phil N. Smith

Applied five generic MODS weighting methods to weight six economic, environmental and social criteria

An Evaluation of Multiple Objective Decision Support Weighting Techniques in Natural Resource Management

Stefan A. Hajkowicz Application of MCA in multi-stakeholder environmental management decisions Weighted summation method

Supporting multi-stakeholder environmental decisions

Randal Greene, Rodolphe Devillers, Joan E. Luther and Brian G. Eddy

Multiple-criteria decision analysis approaches different methods GIS-Based Multiple-Criteria Decision Analysis

Jacek Malczewski, Claus Rinner Spatial analysis approach Multicriteria Decision Analysis in Geographic Information Science

Jacek Malczewski overview techniques for GIS based land-use suitability mapping and, and identify the challenges and prospects of GIS-based land-use suitability analysis

GIS-based land-use suitability analysis: a critical overview

STEPHEN J. CARVER An introduction to multi-criteria evaluation Principals and techniques Integrating multi-criteria evaluation with geographical information systems

Wang et al., 2009 Reviewed the corresponding methods in different stages of multi-criteria decision-making for sustainable energy, i.e., criteria selection, criteria weighting, evaluation, and final aggregation

Review on multi-criteria decision analysis aid in sustainable energy decision-making

Esri (2011) Spatial analyst, weighted overlay technique. Weighted Overlay. Joerin et al., 2001 Land suitability analysis for housing was realised for a small region of

Switzerland. Using GIS and outranking multicriteria analysis for land-use suitability assessment

van Haaren and Fthenakis, 2011 A method of site selection for wind turbine farms in New York State, based on a spatial cost–revenue optimization

GIS-based wind farm site selection using spatial multi-criteria analysis (SMCA): Evaluating the case for New York State

Charabi and Gastli, 2011 GIS-based spatial multi-criteria evaluation approach, to assess the land suitability for large PV farms implementation

PV site suitability analysis using GIS-based spatial fuzzy multi-criteria evaluation

Garmendia and Gamboa, 2012 Address the critical ―compression‖ phases of participatory multi-criteria evaluation (MCE) processes and explore the issue of criteria weighting

Weighting social preferences in participatory multi-criteria evaluations: A case study on sustainable natural resource management


16

1.3.3.2 Overview of Biofuel site selection studies

Several studies have been done in order to access the potential sites for allocating

biofuel farms, considering different parameters and methods (Batten et al., 2011;

Borowitzka et al., 2012; Das and Salam, 2014; Milbrandt and Jarvis, 2010; Wigmosta

et al., 2011). Similarly, the majority of these studies identify the importance of site

selection in terms of resource availability (Quinn et al., 2012). A list of studies for

allocating biofuel production site are listed in Table 2.1.

Walmsley et al. (1999) used NRMtools decision support framework to integrate

economic assessment of alternate land allocation strategies with spatial land use

allocation technologies to generate spatially land use patterns on the basis of economic

optima and land use objectives. The main input criteria of the model were areas of land

in different land use suitability classes, commodity prices and production costs, then

the model determined an optimal economic expansion and allocated the total sugar

cane of a catchment across different land use classes (Walmsley et al., 1999).

Zhu et al. (2001) have adopted Multi-criteria modelling and GIS to evaluate

different land allocation scenarios for sugarcane production along with the values of

stakeholders. The model evaluated the feasibility of land for sugarcane based on the

rank orders of importance of criteria using SMARTER technique. For case-study region

in Lower Herbert Catchment in Queensland with high effective sugarcane industry, land

use constrained and allocation criterion maps were produced. It has been represented

that how the result might be different under using various allocation criteria and the

importance of defining precise land use constrain and allocation criteria, however the

model isn’t applicable for multiple land allocation and the selected allocation criteria

were limited to slope, distance to mills and roads (Zhu et al., 2001).

Mallawaarachchi and Quiggin (2001) provided a method for analysing economic –

environmental trades-off in land allocation for sugarcane. The main purpose of Cane

Land Allocation Model-Herbert (CLAM-Herbert) was to investigate the socially optimal

strategy for allocating land at regional level between sugarcane, other production and

conservation.


17

The regional value of available land and site characteristic such as slope and

elevation and the opportunity of using that land were the main factors of the model. As

the result the model classified land to good, average and marginal according to the

cane production price (Mallawaarachchi and Quiggin, 2001). The model didn’t consider

climatic parameters and water availability in its assumptions despite of being highly

important and necessary to be investigated.

Perpiña et al. (2013) applied a GIS-MCA technique for identification of suitable

sites for locating biomass plants in Utiel-Requena, Spain. They investigated the

influence of selection of factors and criteria such as slope, crop type, land use, transport

cost and socio-economic factors in evaluation of potential sites. Mapping economic,

environmental and social aspects of land showed the least to the most suitable sites

for bioenergy plants. Furthermore, they performed the sensitivity analysis for the

different factors involved in MCE, which showed strong influence of chosen criteria such

as physiography and crop types in the model result (Perpiña et al., 2013).

Das and P. Abdul Salam performed Suitability analysis, using Geographic

Information System (GIS) to develop a generic methodology for the inspection and

assessment of microalgae cultivation potential over a province in Thailand. Their study

included two stages: Stage 1) comprises of examining the availability of the site

considering all the factors influencing the cultivation of microalgae. Stage 2) depicts the

theoretical calculation of the potential of biomass from microalgae. There considered

several criteria for the implementation of algae cultivation unit like climate, water, land,

nutrients and carbon supply, as this all factors affect the quality of the production as

well as quantity (Das and Salam, 2014).

In another study, the identification of the optimum sites for industrial-scale

microalgae biofuel production using a GIS Model, performed by Algae R&D Centre,

Murdoch University, WA, Australia. In terms of the criteria, their climatic considerations

include the amount of incoming solar radiation, minimum daily temperatures, length of

the growing season, the amount of precipitation and evaporation, and the frequency

and intensity of severe storms. Land requirements, consisted of large tracts of level

topography with workable soils are important as well as land that can be purchased for

a reasonable price. Furthermore, impact on cultural values, environmental sensitivity


18

and the economic viability of production considered in their study. In addition, CO2 and

nutrient availability in the form of nitrogen and phosphorus included in the effective

factors(Borowitzka et al., 2012).

Due to challenges around resources availability for algae production, Prasad et

al. (2014) mapped regional hotspots for growing algae according to the availability of

nutrient resource requirements. They also quantified potential regional biomass

production based on the limiting resources in those regions (Prasad et al., 2014). The

suitability map generated considering both available waste nutrients and eco-climatic

parameters showed the most suitable areas for establishment of algal ponds in

Queensland with the potential production of 309 ML of biodiesel which is 5% of

Queensland’s 2011 diesel oil sales.

Milbrandt and Jarvis in a study, provide understanding of the resource potential in

India for algae biofuels production and assist policymakers, investors, and industry

developers in their future strategic decisions. They considered climate, water, CO2,

other nutrients, and land as the critical resources for algae production systems in India

and used GIS technology to analyse the collected information and visualize the results.

The study considered stationary CO2 sources in areas where these facilities coincide

with other inputs necessary for algae growth or conditions that meet the engineering,

economic, environmental, and social requirements for this technology (Milbrandt and

Jarvis, 2010).

To answer “Where” at a greater geographical scale, Batten et al. (2011)

investigated suitable locations for algal production globally in APEC economies for the

sustainable production of biofuels. They developed a geographical information system

(GIS) based model to rank the potential algal site and their production based on solar

radiation, CO2 sources and available land. The model output suggested that the most

preferred sites in Australia are on its marginal coastline, however in Queensland there

are several areas of inexpensive, marginal land near the coastline that could be good

for growing algae (Batten et al., 2011).


19

Moreover, a variety of studies incorporate site selection with economic factors

for suitable large scale production location. For instance, Quinn et al. (2012) generated

a dynamic map based on economic evaluation for CO2 transport distance, and land

resource data for algal production in several key regions of the USA. The validated

growth model predicted biomass production and CO2 economic evaluation. The GIS

land availability was defined based on land classification and maximum slope. The

results of both models are dynamic maps illustrating current production locations and

corresponding productivity potential (Quinn et al., 2012).

In Queensland a variety of land evaluation and classification approaches has been

used as a basis of protecting agricultural land and supporting the agricultural sector

since the early 1990s, including the lapsed State Planning Policy 1/92, statutory

regional planning, the Strategic Cropping Land framework and more recently through

the Agricultural Land Audit, draft State Planning Policy 2013 and reforms to the

Vegetation Management Act of 2013. As a part of goal to identify and plan for additional

future food production land in the state, the Queensland Land Audit (The Audit)

mapped current and potential land uses across the state for different land use

classifications of various crops, including sugar cane. As a part of goal to identify and

plan for additional future food production land in the state, the Queensland Land Audit

used certain principles have been identified as applicable in Queensland .these

principals draw on the Food and Agriculture Organization (FAO) Framework for Land

Evaluation (FAO, 1976; FAO, 1983) which has been the primary approach used

worldwide (Department of Natural Resources and Mines and the Department of

Science, 2013). The Audit mapped current and potential land uses across the state for

different land use classifications of various crops, including sugar cane. The

Queensland Land Use Mapping Program (QLUMP) provided the current land-use

datasets used in the Audit. Land potential was determined by the Audit through an

approach largely based on the established Agricultural Land Classification for strategic

planning in Queensland published in Guidelines for Agricultural Land Evaluation in

Queensland (Audit, 2013). The Audit uses a desktop based method analysing existing

datasets or data developed from existing datasets, and presenting them using existing

tools and expert knowledge in a Geographic Information System.


20

The Audit combined the spatial datasets such as, socio-economic and climatic

data to identify and map agricultural land use potential. The results showed the

potential area for growing sugarcane in Queensland is almost 7 million hectares or 4.1

percent of the state while Current land use Is 0.33 percent of the state. It has been

indicated that in this study, access to a sugar mill is an important consideration in

determining the potential for land to be used for growing sugarcane (Audit, 2013).

In spite of biofuel research inputs to date, producing algal biofuel at a national

supply scale is still an unfulfilled vision in Australia and studies are dominated by the

production process and cost at the plot-level (Li et al., 2012). To our knowledge, there

has been no study using mixed criteria (biophysical, economic and environmental) in

Queensland to identify the suitable sites for larger scale biofuel production, which may

be more cost-effective. Hence, this study can cover the gap knowledge between actual

and potential land use for biofuel production. Therefore, as a priority for detailed further

investigation, this study based on reliable basic information, would obtain important

results such as the land use for bioethanol production regarding to eco-climatic,

economic criteria and land use factors. The criteria used in this study included:

temperature, sunshine, rainfall, evaporation, wind speed (climatic), land value,

transportation cost, labour costs (economic) and ownership, land cover, agriculture,

wasteland, forest, industrial, slope, cultural value (land use). Suitability analysis

performed on these key factors would locate the potential site for biofuel production in

QLD.

This research would be the first study which identify the most suitable sites for

biofuel farms with the greatest potential for long term economic and environmental

sustainability, as a basis for any investment in large scale biofuel production in

Queensland, Australia.

The University of Queensland | School of Geography, Planning and Environmental Management 21

Table 1.2: Biofuel Site Selection Studies

MAXWELL, E. L., FOLGER, G. & HOGG, S. E. 1985

Climate, land, and water resource requirements of microalgae production systems (MPS) were examined relative to construction costs, operating costs, and biomass productivity.

Resource evaluation and site selection for microalgae production systems

FARRELL, J. & SARISKY-REED, V. 2010

Identifying challenges in the production of economically viable, environmentally sound biofuels.

National Algal Biofuels Technology Roadmap

LUNDQUIST, T. J., WOERTZ, I. C., QUINN, N. & BENEMANN, J. R. 2010

Assesses the economics of microalgae biofuels production through an analysis of five production scenarios.

A realistic technology and engineering assessment of algae biofuel production

WIGMOSTA, M. S., COLEMAN, A. M., SKAGGS, R. J., HUESEMANN, M. H. & LANE, L. J. 2011

Providing a detailed screening of required on‐site land and water requirements

National microalgae biofuel production potential and resource demand

KLISE, G. T., ROACH, J. D. & PASSELL, H. D. 2011

The model uses spatially referenced data for nitrogen and phosphorous, CO2, land cover and solar insolation to identify optimal locations.

study of algal biomass potential in selected Canadian regions

QUINN, J. C., CATTON, K. B., JOHNSON, S. & BRADLEY, T. H. 2012

Geographical Assessment of Microalgae Biofuels Potential Incorporating Resource Availability growth system.

Geographical assessment of microalgae biofuels potential incorporating resource availability

Borowitzka et al. 2012 Site targeting approaches used to identify appropriate locations for algal biofuel production facilities.

Identification of the optimum sites for industrial-scale microalgae biofuel production in WA using a GIS model

Zhu et al. (2001) Sugarcane land allocation modelling which integrates Multicriteria and GIS

Integrating Multi-Criteria Modelling and GIS for Sugarcane Land Allocation

Perpiña et al. (2013) Identifying suitable areas for locating biomass plants using MCA-GIS method

Multicriteria assessment in GIS environments for siting biomass plants

Das and Salam, 2014 Reviews and develop a generic methodology for assessment of microalgae cultivation potential site.

Development of a Generic Methodology for Assessment of Microalgae Cultivation Potential Using GIS

Prasad et al., 2014 Mapping the availability of the three inputs for algal cultivation (N, P and CO2) together with climatic and land use considerations

Facilitating access to the algal economy – mapping waste resources to identify suitable locations for algal farms in Queensland

Milbrandt and Jarvis, 2010 Understanding of the resource potential in India for algae biofuels production

Resource Evaluation and Site Selection for Microalgae Production in India.

1.4 Approach

Outside of the necessary nutrient requirements (Verdoodt and Van Ranst, 2006),

the importance of seasonal and regional climatic parameters influence on crops growth

is not negligible (Elbehri et al., 2013; Wigmosta et al., 2011).In this research Three main

factors which are land use factors, climatic factors and economic factors (Klise et al.,

2011; Singh and Gu, 2010) were evaluated. Biophysical growth requirement, land use

and socio-economic data obtained from Queensland state government and national

databases sources (ANU, ABARE, ABS and BOM) and then matched against

Queensland local government authority boundaries (Table1.3).

Table 1.3: Requirement variables and data types

Variable Data Type

Climatic Temperature, Sunshine, Rainfall, Evaporation, Humidity

Economic

Land Use

Land Value, Transportation cost, Labour costs

Ownership, Land cover, Agriculture, Wasteland, Forest,

Industrial, Slope, Cultural value

The methodology for this study, divides into two objectives, suitability and

evaluation. In this thesis, I employed ArcGIS application and Multi-criteria analysis

(MCA) for evaluation and developing map data layers though spatial modelling,

identifying the suitable land for algae production. Multicriteria evaluation technique was

used to perform suitability analysis. Within ArcGIS software package, the ‘Overlay

toolset’ in the ‘Spatial Analyst toolbox’ includes three tools that support suitability

modelling and site selection: weighted overlay, weighted sum, and fuzzy overlay. In this

study, weighted overlay tool has been adopted to find the potential sites (Malczewski

and Rinner, 2015). This approach described in chapter two in details.

In the third chapter, map overlay technique used to investigate the potential site for

both algae and sugarcane production. In this section, algae suitability map from chapter

two overlayed on sugarcane potential site map produced by Audit. The aim was to find


23

out if algae could be produced in sugarcane production sites in case of land use change

consideration in future.

1.5 Thesis Outline

This thesis includes four chapters which are shown in a schematic overview (Table

1.4). The first chapter consists of a brief description of the problem and the motivation

of the study, the research aim and the objectives followed by a literature review section

which presents the past studies and the knowledge gaps which this research intends

to address. The following two chapters (2-3) are presented as a set of publication-ready

articles that each address the research objectives. Chapter two addresses the first and

second research aims and reviewed biofuel/algae growth and production literature and

also different methods of suitability evaluation models. In this chapter an algae

suitability map was produced and the influence and the strength of each factor on the

suitability model were presented. The third and fourth research questions were

addressed in chapter three. In this chapter, a comparison of the sugarcane production

locations with the algae suitability map with the option of co-location were studied. The

last chapter of this thesis concludes with a synthesis of the previous chapters and a

general discussion on this study.

Table 1.4. Schematic overview of thesis structure; each boxes indicate chapters comprised of

research articles which individually address the main research questions of the study.

•General introduction Research aim and objectives Study area.CHAPTER1

• Site selection for commercial microalgae production using multicriteria GIS modelling in Queensland, Australia.

CHAPTER2

•Assessing the land use implications comparing sugarcane and algae for bioethanol production according to economic efficiency and land use concerns.

CHAPTER3

CHAPTER4 .Synthesis and Conclusion.


24

CHAPTER 2. Site selection for commercial microalgae cultivation using multicriteria GIS modelling in Queensland, Australia

2.1 Introduction

There is greater global attention on the potential of biofuel as a sustainable energy

source; influential factors include: growing energy consumption, increasing fossil fuel

prices and mounting concerns over greenhouse gas emissions. In Australia biofuels may

be economically produced on rural land without competing with agriculture or

conservation, and may potentially provide economic opportunities for development and

employment. Total primary energy consumption is projected to grow by nearly 42

percent by 2050 in Australia, therefore the demand for energy is expected to continue

to mount. Hence it is critical to investigate cost-effective investment for Australia’s

energy future and for achieving its government legislated Renewable Energy Targets.

Our study focuses on finding suitable land for growing microalgae as a biofuel

resource as it is considered as one of the best feedstocks for sustainable biofuel

generation. This research investigated suitable areas in the state of Queensland for

growing microalgae; Queensland was chosen because of its land areas, favourable

growing conditions and available data on land values for economic assessment. This

study considers a number of criteria as part of a GIS land suitability analysis, including

biophysical parameters affecting growth, climatic and environmental constraints, site

access and remoteness along with land values.

The outcomes of this research advance the techniques for biofuel site

assessment and provide comprehensive and accurate results which can support the

microalgae-based biofuel industry development in Queensland and evolve better

management strategies for sustainable land use planning in the state.

Demand for energy is expected to grow during the next few decades in Australia

(Geoscience Australia and BREE, 2014) and the energy consumption is projected to

increase by 63 per cent by 2029-30 (Commonwealth of Australia, 2007). Australia’s

combined dependency on crude oil and fuel imports for transport has grown from

around 60% in 2000 to over 90% today (Biofuels Association of Australia, 2014) and it

is projected to increase to 76% by 2030 (ABARE, 2010; Geoscience Australia and

BREE, 2014).


26

From an environmental perspective, greenhouse gas emissions (GGE) in

Australia have grown by 24.7%, since 1990, which is mainly caused by the electricity

and transport sector (Department of Environment, 2015). Rising concerns about

climate change and increasing awareness of the relationship between climate change

and sustainability urged the Australian government to develop the Clean Energy Future

plan. The plan is directly aimed at mitigating the impacts of climate change by

ambitiously targeting to cut GGE by at least 5 per cent compared with 2000 levels by

2020 (Commonwealth of Australia, 2011; Department of the Senate, 2007).

Recently liquid biofuel has attracted huge interest as an alternative source for

transportation fuel as it is similarly energy efficient than fossil-derived fuel and has low

emissions. Energy security, mitigating greenhouse gas emission, biodegradability and

socio-economic opportunities for rural areas are significant advantages of using

biofuels (Batten et al., 2011; Campbell et al., 2009; Demirbas, 2009b; Keating and

Carberry, 2010; Puri et al., 2012).

However, current first generation biofuel crops, such as sugarcane (for

bioethanol) and oil palm (for biodiesel) often stand in direct competition with food

production and/or conservation of previous biodiverse landscapes, such as tropical

rainforests. Microalgae in particular have gained wide attention as a potential source of

biofuel. This commodity suits non-arable lands, utilises virtually any source of water,

may uptake waste CO2 sources and produces many profitable by-products, alongside

co-benefits of GGE mitigation. Furthermore, microalgae are superior in productivity

compared with plant crops in land area requirement and water consumption for

cultivation feedstock. They are considered as a reliable and continuous supply of fuel

due to their high oil content and continual-harvest characteristics (Brune et al., 2009; Li

et al., 2008; Pate et al., 2011; Pittman et al., 2011; Schenk et al., 2008; Singh et al.,

2011). However, to establish microalgae as a successful biofuel crop in Australia or

elsewhere, production costs must be considerably reduced. Identifying suitable

locations for their cultivation can contribute to this goal.


27

In 2009, the Australian government legislated a Renewable Energy Target (RET)

of 20 per cent by 2020 in-line with its national plan for a clean energy future

(Commonwealth Of Australia, 2014) . Currently only 0.5% of Australia transport fuel is

supplied from biomass (ABARE, 2010; Geoscience Australia and BREE, 2014). The

expected large gap between future demand and potential domestic supply requires

expanding viable economic biofuel production in areas which have the land and the

climate needed to produce raw feedstocks on a large scale. In practice, to date, the

lowest cost of commercially produced microalgal oil is still much higher than the

reasonable medium-term price target to become cost competitive with petroleum diesel

(Borowitzka et al., 2012; Stephens et al., 2010).

One of the key factors for a technically and economically viable biofuel resource

is that, it should be competitive and cost less than petroleum fuels (Brennan and

Owende, 2010). The cost of petroleum-based fuels may go up in the future as fossil

fuel reserves decline and policies are put in place to account for the hidden costs

associated with GGE and fuel combustion pollution that pose significant threats to

human health, food security, biodiversity and degradation of natural ecosystems.

However, the commercial production of algae is limited due to challenges

around water and nutrients availability, harvesting methods and high costs of oil

extraction (Prasad et al., 2014; Singh et al., 2011; Stephens et al., 2010). Other factors,

such as land value, land availability, facilities cost, existing land use, closures to

resources and infrastructures, climate requirements, government policies and supports

have also been reported as fundamental barriers to development of the biofuel industry

(Borowitzka, 2013; Coleman et al., 2014; Mata et al., 2010; Maxwell et al., 1985;

Wigmosta et al., 2011).

A large body of literature exists on microalgae production in terms of

theoretical, technical, environmental, economics and implementation to support the

basis for development of government policy and/or industry investment (Brennan and

Owende, 2010; Hu et al., 2008; Li et al., 2008; Lundquist et al., 2010).


28

The majority of these studies consider economic factors as the most common

criteria in their modelling, based on biomass type/resources and final market analysis.

Recently there is growing interest to examine how these opportunities vary across

space and ideal land use suitability allocation (Borowitzka et al., 2012; Coleman et al.,

2014; Das and Salam, 2014; Klise et al., 2011; Maxwell et al., 1985; Quinn et al., 2012).

However, only a few researchers address the overall relations and constraints among

economic, climate and land use factors using available government datasets and

national databases.

In spite of biofuel research inputs to date, producing algal biofuel at a national

supply scale is still an unfulfilled vision in Australia and studies are dominated by the

production process and cost at the plot-level (Li et al., 2012). To our knowledge, there

has been no precise study in Queensland to identify the suitable sites for larger scale

microalgal biofuel production, which may be more cost-effective. This study aimed to

identify the most suitable sites for microalgal biofuel farms with the greatest potential

for long term economic and environmental sustainability, as a basis for any investment

in large scale biofuel production in Queensland, Australia.

The present study integrates state-wide scale data about various factors and

performs spatial analyses for feasibility evaluation and location optimisation based on

methods developed by (Shi et al., 2008). We use a Geographical Information System

(GIS) and methods for Multi-Criteria Analysis (MCA) for mapping and analysis as these

have been used in many energy facility sitting studies (Baban and Parry, 2001;

Malczewski, 2004; Wang et al., 2009).

Our study develops a geographical land suitability model to locate feasible spatial

locations for microalgae production, considering factors for land availability, land cost

and existing land use, climatic variables intrinsic to algae growth and transportation

infrastructure proximity. Each criterion was weighted based on its importance to

productivity and cost-effectiveness. This suitability model provided an economic

assessment of the feasible locations with accurate and updatable map results which

address the gaps in knowledge between actual and potential land use.


29

The results are discussed in terms of implications for better management strategies

in sustainable land use planning in Queensland and support for decision making and

further work towards a sustainable bio-economy.

2.2 Study Area and Materials

The following sections provide an overview of the study area and biofuel

development in Queensland, Australia, the essential criteria, data scale and sources

used in spatial information system (GIS) and the multi criteria analysis technique, based

on climatic, land use and economic resources evaluation. This study is both a suitability

and optimality analyses as we consider cost-effective parameters such as land value,

transportation in our modelling to optimise the locations for scaling up biofuel

production.

2.2.1 Study Area

This study is conducted across the state of Queensland. It is the second

largest state in Australia including 1.9 million square kilometres of land and over 4.5

million citizens. Queensland has sub-tropical and tropical climate. The maximum daily

average number of bright sunshine hours across the state is 8 hours and the average

mean annual temperature is 21°C in the south to 27°C in the north. Fourteen Statistical

Divisions in Queensland are wet in marginal land along the east side with average

annual rainfalls of 1000 mm in the south and 3200 mm in the north and dry to semi-dry

towards the inland west with average annual rainfalls of less than 200 mm (Bureau of

Meteorology, 2015)(figure 2.1).


30

Figure2.1: Study area-Queensland Government Boundaries (source: Queensland Government) and Major climate classes and the average rain fall (source; Bureau of Meteorology, 2015)

The state economy is primarily based on strong mining, agriculture, tourism,

construction, manufacturing and financial services sectors. Queensland's main exports

are coal, metals, meat and sugar. Towards inland Queensland, development pressure

and land value begins to decrease along with vast areas with lesser degree of interest

in all industry sectors. This provides huge opportunities for economical investment in

commercial bioenergy production throughout the study area along with the suitable

climatic and economic factors.

Over the past decade, the ambition to secure the fuel supply and mitigate GGE

based on renewable energy has grown in Australia. A number of sustainable energy

options have been initiated, although algal biofuel production is a rather recent option

in Queensland. Recently, several innovative research projects within Queensland's

renewable energy industry are being carried out with collaborations between private

companies, universities and research institutes in Queensland.


31

Additionally, state funding programs and private industry supports increased the

level of biofuel activity over last few years. Bio-industries map (Figure 2.2) showed more

detailed of major projects and sites in Queensland.

Figure 2.2: Queensland Bio-industries map (source: Queensland (Department of State

Development, Infrastructure and Planning, 2013).

The University of Queensland (UQ)’s Algae Energy Farm, the Solar Bio-fuels

Consortium, and the UQ-led Jet fuel and Culturing Facility of North Queensland

(NQAIF) are the main institutes involved in research on algal biofuel in Queensland (Li

et al., 2012). In 2003/2004, the NQAIF was established within the School of Marine

and Tropical Biology at James Cook University (JCU) through funding by the ARC,

JCU and the Australian Institute for Marine Science.


32

NQAIF is the first tropical microalgal research facility in the world and they have

screened many algal strains that are promising for microalgae biorefinery processing,

including the production of biofuel. Their research areas of interest included

freshwater, marine environments, and a range of paleoclimate studies using fossil

diatoms to identify microalgal strains suitable for biotechnological and environmental

applications (Li et al., 2012).

In 2011, the UQ-led Jet fuel initiative was established with the aim to evaluate the

potential of environmentally friendly aviation-fuel production sourced, among others,

from microalgae. Collaborators include Boeing, Virgin Australia Airlines and US-based

green energy company Amyris , which link with UQ’s biofuel research and biofuel

initiatives of the Queensland Sustainable Aviation Fuels Initiative program(Li et al.,

2012).

2.2.2 Algae Site Suitability

This study focuses exclusively on open pond cultivation of algae (Schenk et al.,

2008). To identify the best location for commercial biofuel production, the methodology

supports two objectives/stages:

Stage (1) - an evaluation of suitable lands availability for biofuel production according

to land uses, eco-climatic parameters.

Stage (2) - suitability modelling and finalising the optimal land selection based on cost

of land, proximity to transportation and labour cost and evaluating the effect of those

costs on the location allocation. Both stages adopted a GIS approach.


33

2.2.3 Resource Evaluation for Biofuel Production Scale up

A major barrier in scaling up biofuel operation is affordability of the production

(Farrell and Sarisky-Reed, 2010). Successful commercial production of biofuel requires

choosing the most suitable locations, according to eco-climatic and socio-economic

considerations. The eco-climatic factors have a critically impact on biofuel productivity

and ultimately cost-effectiveness of the production. Socio-economic criteria consider

the land use and availability of affordable land for scaling up biofuel production.

Three main categories of those factors critical to both site selection and biofuel

production upscaling, are land use, climatic and economic factors which are strongly

spatially dependent. This case study incorporated the unique variables of each of these

criteria. The variables listed in Figure (2.3) are a summary of criteria used in this

application. Those factors were identified from related literatures and large-scale

research conducted at an experimental microalgae farm (Algae Energy Farm) of the

University of Queensland in Pinjarra Hills, South East Queensland. Each one of these

factors requires significant spatial information for allocating a biofuel farm.

Figure 2.3: Essential factors for identifying optimal sites

Algae Suitable SiteSpatial scale >50ha

Flat land

Warm all year

Recyclable water & nutrient

Nearby source of co2

Close to infrastractures

ClimaticTemperature

Sunshine

Rainfall

Evaporation

Economicland value

Transportation cost

Labour costs

Land useOwnership

Land Use/Land Cover

Urban

Agriculture

Wasteland

Forest

Industrial

Slope

Cultural value


34

2.2.3.1 Climatic Variables

Like other biofuel crops, suitable climatic conditions have a direct effect on

microalgae productivity and operation costs (Maxwell et al., 1985). Critical climate

parameters used in this study were temperature, solar radiation, the number of daily

sunshine hours, precipitation, evaporation and wind speed which are hugely

geographically dependent. To investigate a finer scale of climatically suitable sites, the

climate criteria were narrowed to: annual average daily temperature between 15°C -

35°C, minimum winter and night time temperature ≥7°C, annual average cumulative

sun hours ≥ 2,800 and annual average frost-free days ≥ 200 (Batten et al., 2011; Farrell

and Sarisky-Reed, 2010; Wigmosta et al., 2011). The Bureau of Meteorology climate

spatial maps data, based on 30 years of daily records of precipitation, temperature,

evaporation, wind, and solar radiation in Queensland, were used for eco-climatic

modelling.

2.2.3.2 Land Use Variables

To access feasible areas for microalgae production, land use and land cover

are major constraints. Avoiding any conflict with other land use interest,

environmentally and politically sensitive areas should be excluded from consideration.

These include certain areas, such as urban, agriculture, waste disposal land, and

national parks, industrial and cultural lands. The size of land for algal farm is another

economic consideration. With small facility size, the profitability of business is quite low

due to the high capital costs for establishment and low revenue stream (Stephens et

al., 2010).

In some studies the minimum required land size to establish an open pond

facility for biofuel production is considered at least 400 hectares (Campbell et al., 2009;

Quinn et al., 2012; Wigmosta et al., 2011), but in the longer term, smaller size farms

(e.g. 50 hectares) could be considered (Prasad et al., 2014). In this study, the minimum

commercial farm size for the production of microalgae biomass for fuel was evaluated

at the 50 hectare threshold.


35

Since sitting an open pond needs relatively flat land, the other restriction for

algae development is topography. Techno-economical studies consider suitable slopes

at 0–2%. Although the areas with slopes 2–5% are feasible, those areas are less

economical. Areas with slopes greater than 5% are uneconomical due to increased

capital costs for preparation and levelling for site development (Farrell and Sarisky-

Reed, 2010; Quinn et al., 2012; Stephens et al., 2010). Hence, in this study, land

availability in the Stage 1 suitability analysis of algal biofuels potential was limited to

the areas with slopes <2%.

2.2.3.3 Economic Variables

As previously discussed, the production cost of biofuel needs to be

competitive with fossil fuel. Capital costs begin with land purchase and all subsequent

production and processing steps add costs to the algae-to-biofuel supply chain and

need to be considered precisely.

Land value mainly depends on where the land is geographically located, it’s to-

date land use and land ownership (Lundquist et al., 2010). For example, some studies

suggested marginal lands near coastlines as suitable sites, but considering other

interests, such as tourism on those lands make them non-affordable for algae

production. Land value in location decision is clearly a major constrain in the economic

side of scaling up biofuel farms.

For biofuel production, operating costs include charges associated with

transportation, labour and maintenance (Sun et al., 2011) that have to be considered

in economic evaluations. Proximity to infrastructure is an obstacle in locating biofuel

industry. Generally, transportation distances for water, nutrient, pond maintenance and

energy supply to market need to be minimised when determining the economically best

locations. Poor road assess is a major cost factor in rural areas for algae farm

operations.


36

Another input for economic analysis is labour cost, which can also vary by

geographical location. In rural areas it might be higher as there is lower population and

less interest in working in remote areas. To fill the gap between the wages considered for

labour cost in a laboratory in an urban area and a farm in rural area, the amount was

multiplied by the government labour coefficient in rural areas to adjust the estimated

actual labour-cost.

2.2.4 Data Sources

Table 2.1 summarises the publically available Queensland government and

national database sources and types of data used as the essential variable inputs in

the suitability modelling of this study. Noting that, the data which I accessed has the

minimum mapping unit 90m2 specification.


37

Table 2.1: Variables, data sources and type, ideal condition of criteria used in study

Variable Data type Data source Ideal condition

Solar radiation Raster, cell size 250m, monthly

ANU2011 Maximize Wm-2 /day

Temperature Minimum, Maximum, Raster, cell size 250m,

monthly.

ANU2011 15°C - 35°C daily and ≥7°C winter and nightly

Precipitation Minimum, Maximum, Raster, cell size 250m,

monthly.

ANU2011 Maximize mm/day

Evaporation & Humidity

Minimum, Maximum, Raster, cell size 250m,

monthly.

ANU2011 Minimize mm/day, Minimize %

Land use Raster, primary land use codes, size250m.

ABARES2010 Exclude: urban, national parks,

wasteland, industrial and cultural lands

Land value Raster, primary land use codes, size250m.

ABARES2010 Minimize $/ha

Slope Raster, GEODATA 3Sec DEM

Geoscience Australia, GEODATA 3Sec DEM

1-2%

Proximity to the infrastructure

Raster, size 90×90 Department of Natural Resources and Mines

Maximum distance 2km

Labour cost by region in Qld

Average, Statistics ABS Average


38

2.3 Methodology

2.3.1 Suitability analysis

2.3.1.1 Overview of multi-criteria analysis

GIS-based multi-criteria analysis (Joerin et al., 2001; Malczewski, 2004) was

used as it has been widely used for renewable energy analysis problems, involving

economic, technical and environmental criteria (Carver, 1991; Charabi and Gastli,

2011; van Haaren and Fthenakis, 2011; Wang et al., 2009).

Multi-criteria analysis (MCA) requires criteria to be standardized and transformed

to the same unit of measurement in order to be compared and integrated. Therefore,

each criteria layer is reclassified within the range of 1-10, where the feature class with

the most favourable is assigned the value of 10 and the feature class with the lowest

potential favourable is assigned the value of 1. Then each input raster is weighted

according to its contribution to the project purpose or its percent influence. The weight

is a relative percentage, and the sum of the percentage influence weights must equal

100. By running the weighted overlay tool, the cell values of each input raster are

multiplied by the raster's weight (or percent influence). The resulting cell values are

added to produce the final output raster (ArcGIS 10.3.1 help; Jankowski, 1995; Kaliraj

et al., 2015; Malczewski, 2006).

Suitability analysis using multi-criteria evaluation technique, performs within below

steps:

Step 1: Selection criteria

In this case study, the particular criteria were developed based on literature

detailing microalgae biophysical growth requirements, economic constraints in scale up

production and socio-economic and land use factors. Related spatial data were

obtained from government and national database sources such as ANU, ABARE, ABS

and BOM (Table 2.1) for the state of Queensland.


39

Step 2: Data preparation

Data preparation involved two stages. In first stage, land uses considered

unsuitable for algae farms were identified and used to narrow the area of interest for

further analyses. Land uses for urban, parklands, agriculture, wasteland, forest,

industrial and other restricted uses were combined into an excluded data layer. Cultural

land uses such as aboriginal land were also excluded as this was considered different

to develop, but may be considered in the future. A slope layer was also developed to

screen data for land with an average slope less than 2%.

The second stage developed economic land consideration. Land valuation was

only available at a parcel scale from government sources (ABARES), but these data

have many gaps in locations without a land use and inconsistencies in reliable

evaluation of land values. To obtain a continuous spatial layer for land value we

spatially interpolated between parcels where we had reliable data. Interpolation using

a Nearest Neighborhood method in GIS (ArcGIS Help 10.3) was required to ‘fill in’ these

missing land values. In this method, the value of the output cell is determined by the

nearest cell value on the input grid which is specified in the neighbourhood. The nearest

neighbourhood method assigns the value from the nearest observation to a certain grid

cell.

Road data was buffered to determine land within 2 km distance to roads. The

spatial data included vector feature data for land uses and raster data for slopes and

climatic variables. All data was converted to a raster with a geographical cell resolution

of 3 seconds (approximately 90 sq. meters) for further analyses. For distance analysis,

the Distance tools in Spatial Analysis toolset, allow us to perform distance analysis.

Euclidean Distance in Distance tools gives the distance from each cell in the raster to

the closest source. In this study, this method was used to find the most suitable location

close to roads.

Step 3: weighting data

In this step to find out the weighting of each criteria, we used meta-analysis. Meta-

analysis is the systematic review of a body of evidence. The idea is to draw together all


40

of the appropriate studies that have addressed the same question, and calculate an

overall effect and an overall measure of uncertainly for that effect (Crawley, 2012).

Then, the effect size and a variance for each criteria in related studies calculated. The

idea is to calculate an effect size and a variance for each criteria in related studies. The

summary of the meta-analysis is then just a weighted average of these effect sizes.

For this research, several literatures related to biofuel production were reviewed

and from them a list of most to less important factors in algae cultivation was obtained.

Their citation frequencies for criteria were used to derive importance rankings and

weights; the calculated results are presented in Table (2.2).

Table 2.2: Frequencies, importance ranking and weight criteria calculated using Meta - Analysis

*: Criteria included in study, - : Criteria hasn’t included in study

2.3.2 Reclassification and suitability modelling

The reclassification uses for quickly and easily reclassify data which will be used

in spatial analysis. The reclassification tool enable the user to reclassify raster data and

the values in the input raster can be replace with new values considering preference,

sensitivity, priority of the new situation. It can be done in a table which its format allows

the mapping of individual values, ranges of values, strings, or NoData to another value,

Factors Sunlight Temperature Precipitation Evaporation Slope Land-Use Land Value Proximity to Road Labour Cost

Maxwell et al., 1985 * * * * * * - - -

USDOE, 2010 * * * * * * * * *

Lundquist et al., 2010 * * * * * - - -

Wigmosta et al., 2011 * * * * * * * - -

Klise et al., 2011 * - - - * * - - -

Quinn et al., 2012 * * * * - - *

Borowitzka et al. 2012 * * * * * * - * -

Milbrandt and Jarvis, 2010 * * * * * * - - -Frequency 9 8 6 7 9 9 3 2 2

Ranking 1st 2nd

5th

4th

2nd

3rd

6th

7th

7th

Num (total 35) 7 6 3 4 6 5 2 1 1

Weight 0.20 0.17 0.09 0.11 0.17 0.14 0.06 0.03 0.03

-Karabee Das and P. Abdul

Salam, 2014* * * * * * * -


41

or NoData. Based on the criteria’s influence on suitability modelling, all criteria map

layers were reclassified.

Eco-climatic and land use data reclassified in order to be used in MCA modelling.

For instance the areas with the highest solar radiation, lowest slope percent and land

value weighted 10, and the areas with lowest solar radiation, high slope percent and

land value were weighted 1.

Subsequently, in the weighted overlay tool, each map layer was ranked by

influenced percentage according to its relative importance (Table2.2), and then

combined to create a weighted raster layer. The output map was classified into high,

moderate and low suitability for algal production.

2.4 Results 2.4.1 Eco-climatic map

The quantity and quality of algae production is highly governed by climatic

conditions. A potential site for a microalgae commercial farm for fuel as its primary

product, is where all the major parameters affecting algal growth, such as maximum

and minimum temperature, solar radiation, evaporation, humidity, precipitation, and

slope coincide, are maximised.

Firstly, to show the influence of each above criteria in MCA modelling of eco-

climatic evaluation of the study, using variables and their Influence weighting presented

in Table 2.3, radar plot was produced and shown in figure 2.4, which indicated solar

radiation and slope has the most influence in modelling eco-climatic map. The table

below shows the effective variables, influence weighting and their ranking. As

mentioned in the literature, among climatic parameters, sunlight and temperature are

the most important factors which algae need for growing. Radar plot was used to

compare multiple quantitative variables and it is also useful for seeing which variables

are scoring high or low within a dataset, making them ideal for displaying performance.


42

Table 2.3: Variables and Influence weighting use in Radar plot of Eco-Climate suitability map

Variable Influence Weighting Ranking

Sunlight 0.19 1

Temperature 0.17 2

Slope 0.17 3

Evaporation 0.11 4

Precipitation 0.08 5

Humidity 0.03 6

Figure 2.4: Radar plot of criteria used in eco-climatic MCA modelling.

Secondly, according to eco-climatic and land use criteria (maximum and minimum

temperature, solar radiation, evaporation, humidity, precipitation, and slope) the Eco-

climatic Suitability map was generated. As it’s shown in Figure 2.5, the eco-climatic

map has three suitability classes; good, moderate and poor (Figure 2.5).


43

Figure 2.5: suitability map according to eco-climatic and land use criteria.

According to the multicriteria GIS algae model, algae farms should be located

in north-western toward central Queensland (Figure 2.5). These areas are mostly

marginal lands with ideal/suitable climate characteristics without existing conflict with

other development interests. South East of Queensland is defined as poor suitability

class which cannot be considered for growing algae and biofuel industry.

2.4.2 Algae production suitability map

This paper identifies the potential suitable sites for commercial algae production

in the entire state of Queensland. An analysis was undertaken and the optimal sites for

algae production were obtained by overlaying all the thematic maps in terms of

weighted overlay methods using the spatial analysis tool in ArcGIS 10.3. This included

economically suitable sites for production of algae, considering land value, proximity to


44

roads and reclassified remoteness index (labour availability) map layers combined with

eco_climatic parameters in suitability model.

Also, using variables and their influence weighting (Table 2.4), figure 2.6, radar plot

of criteria used in algae site selection MCA modelling produced. As shown, land value,

slope and solar radiation weight chose higher in the modelling for site selection. The

table below shows the effective variables, Influence weighting and their ranking which

were used in this study for allocating biofuel sites. As mentioned in the text, each criteria

has individual effects on site selection with different weight and ranking. Radar plot of

algae site selection shows all those variable values at one go. The table below shows

the effective variables, Influence weighting and their ranking which were used in this

study for allocating biofuel sites. As mentioned in the text, each criteria has individual

effects on site selection with different weight and ranking. Radar plot of algae site

selection shows all those variable values at one go.

Table 2.4: Radar plot of algae site selection MCA modelling

Variable Influence Weighting Ranking

Sunlight 0.19 1

Temperature 0.17 2

Slope 0.17 2

Land-Use 0.14 3

Evaporation 0.11 4

Precipitation 0.08 5

Land Value 0.06 6

Proximity to Road 0.03 7

Labour cost by region in Qld 0.03 7

Humidity 0.03 7


45

Figure 2.6: Radar plot of criteria used in algae site selection MCA modelling

Based on land value, and eco_climatic parameters, proximity to roads and

reclassified remoteness index, the suitability map result released and classified areas

of Queensland in good, moderate and poor for scaling up algae production (Figure 2.7).

The suitability map shows that a large portion of the state is good or moderate for

microalgae cultivation facilities located mostly in the centre and west of Queensland

which is ideal for biofuel commercial sites as these parts of the state are considered

rural areas with suitable climatic condition with no conflict with agricultural or other

development interests.

The areas as defined good and moderate mostly are marginal lands which has

relatively poor natural condition or is not used for agricultural production with

economically land value. Suitable eco-climatic conditions and inexpensive land in these

marginal lands increase the feasibility for long-term profitable biofuel industries and

development of energy plants at large scale. However economically industrial algae

biofuel production in marginal lands across the study area could be achieved when


46

advanced transportation system build in those areas to have the less transportation

cost.

Figure2.7: Spatial distribution of microalgae production suitability levels

As it’s shown in map, South east of Queensland is classified as poor condition for

growing algae. It’s because, this part of state has high land value as the result of tourism

and agriculture interests in the area, along with unsuitable climatic condition for

cultivation algae.


47

2.5 Discussion

By incorporating the spatial and non-spatial data, the GIS model in this study

provides the first accurate map results of potential sites for commercial microalgae

production with fuel as their primary commodity. However, the data may also be used

when considering the production of microalgae for other purposes, such as feed, food

or higher value products, including the use of microalgae farms as biorefineries. Other

factors should be considered (e.g. transport costs, closeness to market, etc.) under

these scenarios.

Similar to this study, to identify the best locations for constructing commercial-scale

algae-to-biofuel production facilities in Western Australia (WA), almost the same criteria

were used in another study (Borowitzka, et al, 2012). They limited production facilities

based on environmental characteristics such as topography, climate and availability of

CO2 but also incorporated construction considerations such as soil workability.

However, they did not perform an economic analysis in their study.

These maps from the present study are capable of providing precise locations

according to climatic, economic and environmental factors. It provides comprehensive

map-information for better management strategies in sustainable land use planning and

also support for decision making towards a sustainable bio-economy. Although it is

recommended that the investment is made for large scale commercial farms, it is also

necessary to incorporate the stakeholders and locals knowledge and decisions in the

final decision-making process for ultimate optimal site selection.

The main limitation of this work is probably the proximity to the resources and this

should be addressed in follow-up studies. Nevertheless, this study developed an

accurate location model to be easily used in biofuel investment projects across

Queensland and allows flexibility towards weighting and incorporation of other

parameters required for decision making.


48

2.6 Conclusion

This study identified suitable sites for microalgae cultivation in Queensland, in two

stages i) suitability modelling and (ii) economic evaluation, adopting a GIS approach

and MCA techniques. A combination of climatic, economic and land-use criteria,

supported by the literature to date, were transformed into a weighted spatial model.

This technique was used to demonstrate site suitability identification for microalgae

biofuel farms in Queensland with the greatest potential for long term economic and

environmental sustainability. The resulted maps presented the information which help

to identify suitable locations for commercial development of an algal farm.

Queensland has indeed very favourable conditions for investment in the

microalgae biofuel industry. Flat terrain, sufficient solar radiation, sunshine hours, and

warm temperatures along with refineries, mines, agriculture activities and animal

organic waste scattered throughout the state as sources of low-quality water and

nutrients, are the main characteristics of the state as a potential location for algae

commercial production. This study has shown that highly suitable locations for algae

production at commercial scales, are in the North West and along the East of

Queensland. Those areas have the advantages of low land value, non-agricultural land

use coupled with proper climatic condition. Our study provides a robust approach for

further analysis through the incorporation of land value as an economic factor, which

can affect directly the capital cost of development an algae farm.

It is important to note that this study did not include nutrient resources (including

CO2) and water. Therefore, further research is required on the feasibility developing

farms adjacent to the nutrient sources. Also investigation is needed into water

availability, focusing on the location of agricultural runoff collection sites, evaporative

ponds used by the oil/gas production and mining industry, and other wastewater

treatment facilities. I believe that, this study is a base for further investigation in

allocation biofuel farm in Australia to assist policymakers and industry developers in

many ways.


49

Chapter 3: Comparison of potential sites for microalgae and sugarcane as biofuel crops


50

3.1 Introduction

In the third part of this thesis, addressed the last objective of the study by

answering these research questions:

I. Where are the most suitable/potential locations for sugarcane production in

Qld?

II. Comparing with suitable algae production sites, where is the best locations for

both, sugarcane and algae?

Existing and potential sugarcane production areas were compared with algae

production suitable sites. For the first time, this research provided map detailed

allocations which are suitable for both algae and sugarcane productions. The aim is to

assist government and investors have a better details for site selection. ArcGIS

application was used to develop map layer represents the same location suitable for

both crop production. Overlay analysis was applied to generate map presentation,

considering constraint criteria.

Queensland sugarcane is the largest intensive agriculture industry and has a

major contribution to Queensland in economy, social and culture for decades. To have

a better understanding of sugarcane industry importance in Qld, brief industry

information has been included in this chapter.

3.1.1 Sugarcane industry in Qld

Australia is among the major sugarcane producer and exporter countries in the

world, as it is shown in Table3.1 and Figure 3.1.

The main sugarcane production regions in Australia are located in Queensland

north-eastern tropical catchments along the coast and small part of NSW.


51

Table 3.1: Sugarcane production in major countries (by area harvest in 2008), according to

FAO estimates (Food and Agricultural Organization of United Nations (FAO), 2013)

Figure 3.1: Sugarcane worldwide distribution. Source (Food and Agriculture Organization of

United Nations (FAO), 2007 apud FAO, 2013).


52

Sugarcane is currently grown on 565162 hectares or 0.3 per cent of Queensland,

which existing infrastructure such as mills, cane tramways, sugar export terminals and

water supply schemes for irrigation support the production of sugarcane and economic

growth of the region (Figure 3.2) (Audit 2013).

Figure 3.2: Queensland Sugarcane production regions and gross value (Audit 2013).

As its shown in figure 3.3, A high proportion of sugarcane land are located in

northern Queensland regions (Mackay, Burdekin and Far North Queensland) and

southern regions of Queensland, Bundaberg and South Queensland and Wide Bay

Burnett, have the least sugarcane land in Queensland state (Audit, 2013).


53

Figure 3.3: Percentage of current sugarcane land in each region (Audit 2013)

3.1.2 Yes or no to continue sugarcane production

One of the key problem in sugarcane production is uncertainty of climate. Qld

often experiences harsh climate, like drought. Shifts in rainfall and flood pattern and

temperature are forecast as the results of climate change, exacerbating the uncertainty

in sugarcane production sustainability and profitability.

As it is shown in figure 3.4, harvested cane area has been declining in Queensland

between 2000 and 2012, due to seasonal conditions (Department of Agriculture,

Fisheries and Forestry Queensland Government, 2014).


54

Figure 3.4: Harvested sugar cane area and tonnage (DAFF Queensland)

Historically sugarcane production is the major profitable industry in Queensland.

Sugarcane is grown in 26 major river catchments in Queensland, most in environmentally

sensitive areas(Rayment, 2003). Along with the expansion of the industry, the negative

environmental consequences of sugar production are concerned. These concerns extend

to the sea, where discharges of nutrients, sediments and toxicants above natural levels are

unwelcome, particularly when they drain to the Great Barrier Reef World Heritage Area and

other coastal waters of Queensland (Rayment, 2003).

Despite the economic benefits of the sugarcane industry, there is a concern about the

environmental and natural resources issues on sugarcane production (from planting to

harvest) in community such as: i) Habitat loss, cumulative impacts and impacts on

biodiversity, ii) Excessive water consumption in cultivation, iii) Soil erosion, declining soil

health and fertility, iv) Agrochemical use, vii) Water pollution, viii) Sugarcane processing,

viii) Farming marginal lands, ix) release of ashes and greenhouse gases during the burning

prior to harvesting To mitigate these issues one of the options is to change the land use with

another type of crop with less harm to environment and water resources(Andreae, 1991)


55

(Christofoletti et al., 2013; Martinelli and Filoso, 2008). The ability of algae to grow in most

places, with any source of water (Pittman et al., 2011); offers it as an alternative plant for

mitigating the negative environmental effects of sugarcane production along with water

consumption reduction.

Accordingly, this part of the study compares site suitability of sugarcane and algae for

bioethanol production according to economic efficiency and land use concerns. Results are

presented as a detailed map analysis between algae and sugarcane production allocation

in Queensland. The multi criteria evaluation is` used to produce algae suitable allocation

considering land use, climate and economic criteria. Suitable sugarcane production sites

are allocated by the same method using ArcGIS.

This study advanced the information of biofuel production from both sugarcane and

algae. The highlights of this research is providing map results for multiple- use or dominant-

use land managements in Queensland for producing biofuel and bioproducts for the first

time. The outcomes of this study advance the techniques for biofuel site assessment and

provide comprehensive results which can support the microalgae-based biofuel industry

development in Queensland.

3.2 Method

Suitable production locations for sugarcane were drawn from the Queensland

Agricultural Land Audit (the Audit) and compared with algae suitability map generated from

earlier work in this study. ArcGIS overlay application was used to present those areas in

map detail. The Audit was conducted during 2012-13 to identify land important to current

and future agricultural production across Queensland.

The approach used was based on the FAO method (FAO 1976). The main conceptual

steps in land-evaluation in FAO method consisted of i) Initial consultation on the objectives,

ii) Determination of the requirements of relevant land-use options, iii) Mapping land qualities,

iv) Interim matching of land-use requirements with actual land qualities, iiv) Final matching.


56

The Audit considers all land in Queensland other than land that is alienated from use

for agriculture in the long-term. Land suitability classification in Queensland is the evaluation

of soil and land attributes based on the requirements of a specified land use using current

technology and management and Socio-economic factors are considered in general terms

only. Other limitations for assessing agricultural land suitability in Queensland are listed in

Table (3.2).

Table 3.2: Limitation criteria used for assessing agricultural land suitability in Queensland

(Guidelines for Agricultural Land Evaluation in Queensland, Second edition)

Limitations

Land use requirements

Climate, Drainage Water, Wind erosion Water erosion, Subsoil erosion hazard, Flooding, Water infiltration, Soil water availability, Soil physical factors, Salinity, Topography, Nutrients, Vegetation, Pests and diseases

Land excluded from consideration in the Audit includes land permanently inundated,

land gazetted as national parks, defence and other commonwealth purposes, established

mines, existing urban areas and other intensive non-agricultural land uses. Different criteria

were used by the DAFF Qld Agricultural Land Audit (2013) to map potential sugarcane

production areas according to data from the Queensland Land Use Mapping Program

(QLUMP). DAFF mapping is considered an information source for policy and planning

decision-making at a regional level and includes agricultural land class A and class B with

slope less than 5 per cent and fewer than 55 days per year with a minimum temperature of

9°C or less and excludes: land that is urban, under intensive use (such as mining), national

parks, state forests, land managed by the Department of Defence or permanently under

water (Figure 3.5)(Department of Agriculture Fisheries and Forestry Queensland

Governmenrt, 2013) .


57

Figure 3.5: Queensland Sugarcane potential production sites. (Department of Agriculture

Fisheries and Forestry Queensland Governmenrt, 2013) .

Both the algae production land suitability map and sugarcane potential map were

produced according to climatic, environmental and land use considerations. Both crops

requirements in terms of land qualities were previously reviewed and used in the

evaluation process outlined in Chapter 2 and in the Audit, respectively. The major

difference between these maps is economic evaluation, which was considered in the

algae production mapping in this study but was not considered in sugarcane map

assessment by the Audit.

As mentioned in chapter two, the algae map suitability map takes to account land

value, labour cost and proximity to roads as economic factors. These factors are

relevant to both algae and sugarcane production, hence, overlaying those two maps

deliver a product which accounts for economic assessment and other criteria relevant

to the location of multi-use lands.


58

ArcGIS offers accurate geographical map results which can assist decision

makers in determining the spatial suitability of a specific land use in an area. To achieve

the aim of this study, an overlay technique was performed and both maps combined

into one to find the locations suitable for identifying the particular lands proper for algae

and sugarcane or combination of both crops production (Figure 3.6).

The overlay techniques allow the evaluation criterion map layers (input maps) to

be combined in order to determine the composite map layer (output map). This

approach is often used to find locations that are suitable for a particular use

(Malczewski, 2004). In general, there are two methods for performing overlay

analysis—feature overlay (overlaying points, lines, or polygons) and raster overlay.

Overlay analysis to find locations meeting certain criteria is often best done using raster

overlay (although you can do it with feature data). In raster overlay, which were used

for this part of study, each cell of each layer references the same geographic location.

That makes it well suited to combining characteristics for numerous layers into a single

layer (ArcGIS help Desktop 10.1).

In this procedure, overlay analysis performed on algae suitability map from MCA

suitability modelling and sugarcane potential sites map from Audit and the map result

of is presented in following chapter (figure 3.6).

Figure 3.6: The process of the final map production

Algae suitability map

(MCA suitabilty modeling)

Sugarcane potential

sites

(Audit )

Algae and sugarcane suitablity production sites or

combination of both crops.


59

3.3 Results

The algae and sugarcane potential production site map (Fig 3.7) demonstrates

the enormous capacity for development of both crops across the state. This map was

strongly influenced by limitation criteria such as climatic, biophysical, scio-economic.

Hence it provides valuable regional information for commercial interests in

algae/sugarcane production.

Figure 3.7: Queensland Algae and Sugarcane suitable production sites.


60

According to the map, in north of Queensland there are some potential locations

which can be used for algae production combined with sugarcane growing. Other parts

of Queensland with hot seasons, steep slope, low humidity and precipitation with long

distance from infrastructures, limit the production of either sugarcane or algae.

Consequently, those areas are outside of any biofuel investment plan consideration

and have been excluded in this study. Land value is one of the important component of

the capital cost for development of biofuel production facilities. Considering land value

criteria in suitability analysis leads to identification of economically viable biofuel

production sites. Figure 3.7 indicates the overlap locations of algae and sugarcane, are

the lands economically suitable for both crops investment.

However another important factor in sugarcane economic production is proximity

to the mills. In next stage, CO2 resources map and sugarcane mills location map added

to the figure 3.7 to have a better According to the figure 3.8, the best location for

sugarcane and algae facilities development are the places which are near to the mills

and CO2 resources with economically land value. These areas are located mostly in

north east and east of Queensland. In Table 3.3, the selected areas were compered to

each other according to their land value and proximity to mills, CO2 resources and

roads. However some areas are categorised in moderate condition in value analysis

but they could considered economic as their proximity to the mills facilities and CO2

resources (area 4 and area3). In some areas with poor access to the mills and other

resources, investment for commercial production of algae or sugarcane would be more

costly which needs to be considered.

As it is shown in Figure 3.8, this particular area was ranked highly for algae

cultivation. It means it has the suitable climatic, land use and more importantly cheap

land value values. These parameters outweighed the distance to roads criterion, but

clearly this region requires further infrastructure developments. Hence, this location is

chosen as a proper site.


61

Table 3.3: Selected area suitable for sugarcane and algae farm

Selected Area Proximity to mills

Proximity to CO2 resources

Proximity to roads Land value

Area 1 Poor Poor Poor Good

Area 2 Good Moderate-poor Good Good

Area 3 Good Good Good Moderate-Good

Area 4 Good Good Good Poor-Moderate

Figure 3.8: Queensland Algae and Sugarcane suitable production sites close to the mills with economically land value.


62

3.4 Discussion

To mitigate the sugar production environmental and water resources management

issues, two practical land-use management options and justifications are suggested

below:

1. The alteration of the crop production from sugarcane to algae in future land

use management change:

The main reasons for this are:

I. Water resources usage:

A disadvantage of sugarcane, compared with the other crops, is its

potentially high use of fresh water resources. High water use is often required to

achieve the high sugar yields required for economically viable production. Water

use will be an important consideration, particularly in countries such as

Australia(Renouf and Wegener, 2007). In contrast, algae can grow in in fresh,

brackish, waste water or even saline water(Prasad et al., 2014; Quinn et al.,

2012). The algae growing ability in seawater or saline groundwater rather than

freshwater reduce the competition for a valuable limited water

resource(Borowitzka and Moheimani, 2013).

II. Greenhouse gas emissions:

One of the main issues in production and oil extracting of sugarcane is

contribution to greenhouse gas emission. Regardless of how effective sugarcane

is for producing ethanol, its benefits quickly diminish if carbon-rich tropical forests

are being razed to make the sugarcane fields, thereby causing vast greenhouse-

gas emission increases (Scharlemann and Laurance, 2008; Timothy

Searchinger1, 2008).

Renouf and Wegener studied the environmental life cycle assessment

(LCA) of sugarcane production and processing in Australia. Their study showed

the aspects of raw sugar production that contribute to greenhouse gas emissions

in Figure (3.9). Based on the average results, nitrous oxide (N2O) emissions from


63

soil nitrification/denitrification processes are the dominant source (59%). The

other significant sources are electricity for irrigation (20%), transport/machinery

emissions (9%), fertiliser and pesticide production (5%) and bagasse combustion

which releases some methane and N2O (5%)(Renouf and Wegener, 2007).

Figure 3.9: Greenhouse gas emissions for cane sugar, showing contributing activities in

Queensland. (Proceedings of the Australian Society of Sugar Cane Technologists, 29,

2007).

One of the key advantages from algae is the capacity to capture GGE

and reduce their emissions(Campbell et al., 2009; Elbehri et al., 2013). It has the

ability to fix CO2 efficiently from sources like the atmosphere, exhaust gases from

industries and amounts of carbonate salts(Das and Salam, 2014). So a major

advantages of microalgae biomass production is its significant global contribution

to the objectives of renewable and sustainable biofuels and feeds, as well as

greenhouse gas reduction(Klein‐Marcuschamer et al., 2013a).

III. General environmental benefits

This option offers a solution for improving water quality entering the

Great Barrier Reef Lagoon and reducing the water quality impact of

agricultural landscapes. The other positive impacts are particularly noticeable

in the air quality improvement of metropolitan areas but also in rural areas


64

where mechanized harvesting of green cane is being introduced, eliminating

the burning of sugarcane (Goldemberg et al., 2008).

Hence, positioning the suitable sites for both crops would led to better management

of altering the type of crop according to local government and stakeholders’ preference.

Despite of environmental, land use and water quality benefits of the diversion of

sugarcane to algae, competing biofuel with food production would be concern in this

option which needs to be addressed.

3.4.1 Cost effectiveness comparison of producing biofuel from algae and

sugarcane Biofuel production on a commercial scale requires producing oil which is

economically competitive with fossil fuel. Traditionally fossil energy is used to produce

biofuel. Chisti (2008) in his review paper discussed the economics and quality constraints

of biodiesel from microalgae and suggested that, to have a compatible price with

traditional energy sources, the cost of growing microalgae for biofuel production must be

reduced. For example, more energy must be recovered in the fuel compared to the fossil

energy used in its production. In short, the energy ratio of the fuel must substantially

exceed unity. Preferably, the energy ratio should be 8, or more, as is possible to achieve

for bioethanol derived from sugarcane. Estimates suggest an energy ratio of <1 for algal

fuels in many cases (Chisti, 2008). Reducing the energy consumption required for algal

fuels may lead to improve energy ratio (Chisti, 2008). Other sources of energy, such as

solar, offer an exciting opportunity towards a zero ratio in energy consumption for algae

production. This form of production is already being tested at the University of

Queensland algae farm.

The other aspect of algae biofuel project capital costs are expenses for land

infrastructure establishment, bioreactors and labour. The production costs may include

expenses for cultivation (expenses for nutrients); harvesting and dewatering; and

extraction and separation. Besides these, costs include maintenance, components

replacement, transportation and overhead expenses (Parmar et al., 2011; Singh and Gu,

2010). In producing biofuel from sugar cane, lower energy content, high solubility in water


65

and high vapor pressure impact on its cost and employability and raises the total

operating costs. The technology is not sustainable without subsidies and requires more

land and water for biofuel production which leads to significant increasing price of biofuel

production (Hassan et al., 2015).

In 2012, the University of Queensland investigated three process models for the

production of aviation-fuel from microalgae, Pongamia pinnata seeds and sugarcane

molasses. This analysis indicated that the biorefineries processing the microalgae,

Pongamia seeds, and sugarcane feedstocks would be competitive with crude oil at

$1343, $374, and $301/bbl, respectively (all currencies used in the models are based on

2011 US dollars). This economic analysis considered total Capital Investment ($M),

annual operating cost ($M), facility costs, raw materials, utilities, labour cost and

consumables (Klein-Marcuschamer et al., 2013).

However, in another study in University of Queensland new, low-cost technology

has been developed by Peer Schenk and his team and it was aim to producing a cheap

protein source in the form of microalgae to supplement cattle in northern Australia during

the dry season. Major technological advances throughout the project included: (1) the

selection and adaptation of fast-growing, protein-rich, easy-to-harvest, saline- and heat-

tolerant microalgae collected from cattle farms in the NT, (2) a new hydrodynamic pond

design that cuts the cost of mixing cultures by half, (3) a new airlift design for efficient

culture mixing and CO2 supply to ensure rapid growth of healthy cultures, (4) a new, low-

cost harvesting process that uses gravity for induced settling instead of costly

centrifugation, (5) a low-cost solar dryer. The techno-economic analysis has been

performed based on data collected at the Pinjarra Hills farm that was applied to a 10 ha

farm with 8 ha pond surface area (annual production capacity: > 400 tons DM pa).

In their study, related economic factors such as cost of construction, cultivation,

dewatering, drying and CO2 transfer along with cost of water, electricity, maintenance,

engineer wage, labourer wage, lifetime of project and interest rate were considered in

analysis. In their techno-economic model, oil with feedstock production with and without

utilising purchased CO2 and with and without the use of solar panels for electricity

generation were analysed shown figure (3.10) and figure (3.11). The results indicated that

growing can be economical by using solar energy and advanced technologies described

in related paper(Schenk, 2016).


66

Figure 3.10: Technoeconomic analysis for microalgal oil with feedstock production. Shown are operating (OPEX) and capital (CAPEX) costs for a 10 ha farm with utilising purchased CO2 and with (right OPEX column) or without (left OPEX column) the use of solar panels for electricity generation (Schenk, 2016).

Figure 3.11: Technoeconomic analysis for microalgal oil with feedstock production. Shown are operating (OPEX) and capital (CAPEX) costs for a 10 ha farm without utilising purchased CO2 and with (right OPEX column) or without (left OPEX column) the use of solar panels for electricity generation (Schenk, 2016).

Relying on recent study by Peer Schenk lab team, with outstanding economical

results and having the suitability map result for algae/sugarcane production, developing

commercial biofuel industry would be reality for Qld. Developing strategies and cost

estimates for commercial-scale production of biofuel from algae or sugarcane also

depends on government agencies and private company investment in further

investigation of advancing production technologies and other effective cost-factors like

land value.

OPEX ($/kg) Solar OPEX ($/kg) CAPEX (total $) energy (kWh/day)

cultivation 0.11 0.10 1005500.00 22.40

Dewatering 0.15 0.13 80000.00 85.50

Drying 0.01 171300.00 67.00

Oil extraction 0.06 0.05 40000.00 55.42

CO2 1.45 1.45 171800.00 0.00

Solar 190397.87

Labour 0.42 0.42 Total energy

Maintenance 0.21 0.24 230.32

total 2.42 2.39 Electricity total1700150.00

Amortisation of CAPEX 0.29 0.32 Solar total 1890547.87

Total with amortisation 2.70 2.71

Oil cost 9.01 9.03

Relative oil cost 0.50 0.50

OPEX ($/kg) Solar OPEX ($/kg) CAPEX (total $) energy (kWh/day)

cultivation 0.36 0.10 3351666.67 67.20

Dewatering 0.48 0.42 266666.67 256.50

Drying 0.01 171300.00 67.00

Oil extraction 0.06 0.05 40000.00 55.42

CO2

Solar 368792.53

Labour 0.58 0.58 Total energy

Maintenance 0.51 0.56 446.12

total 2.01 1.71 Electricity total4068616.67

Amortisation of CAPEX 0.68 0.75 Solar total 4437409.20

Total with amortisation 2.69 2.46

Oil cost 8.98 8.19

Relative oil cost 0.63 0.56


67

3.5 Conclusion

This research is the first attempt to identify the possibilities of growing algae in

sugarcane cultivation sites for land use change determination in the future or cultivation

algae in waste water and nutrition release of sugarcane farms in order to reduce the

environmental and water resources issues of sugarcane production. My study provides

map information which includes the suitable locations for biofuel production with two main

plants, sugarcane and algae. In locating the proper sites, land value and proximity to the

infrastructures and CO2 resources were considered as the base of analysis. According to

GIS analysis and the resulting map, the best potential locations for both sugarcane and

algae cultivation are the north east and north west of Qld. Along the north west to south

west of Queensland there is a lack of suitable sites of cultivation of both plants. The

reasons are, those areas are already under other land use along with the fact that the

land value is very high and it is not economic.

The findings of this study indicated that there is enormous opportunity for investment

in multi-crop production. This study advances the potential of biofuel production in

Queensland. The potential users of the study’s results include policy/decision makers and

consultants of regional environmental, land use and natural resource management policy

area, local governments and investors. In addition it has been noted that land use

planning is dynamic and complex and worthy of more comprehensive investigation as

part of biofuel production feasibility assessment.


68

CHAPTER 4. Synthesis and Conclusion


69

4.1 Overview

This thesis commenced with a brief summary of the rising demand for energy and

environmental concerns about climate change and GGE emission increment in last

decade. Protecting environment from further pollution and mitigating global GGE are the

most critical priority in Australia parallel with other countries. In rapid pace of searching a

clean energy as an alternative for petroleum oil, biofuel draw the most interest among

other source of energy in recent years. Australia with a large land mass and suitable

climatic conditions is consider as a major country for bioenergy production.

In this thesis, I investigated the potential sites for growing algae in Queensland

considering potential constrains such as climatic, environmental and socio-economic

factors (chapter 2). The third chapter consisted of comparison between algae and

sugarcane production potential sites. The aim was mitigating the environmental and

resources management issues of sugarcane production in Qld. Proposed solutions

include changing the land use from sugarcane to algae or growing algae at the same

sugarcane production sites to consume the Co2, P, N and waste water produced. Those

chapters covered the detailed response to the objectives of the study. This final chapter

presents a synthesis of the main findings of the research and the contribution it has for

the study area of Queensland. Also, this chapter presents the main limitations and further

study needs to be addressed in future, followed by a short conclusion.

4.2 The contribution of biofuel production

This thesis makes an important contribution to building biofuel production and its

advantages for Queensland. At present, biofuel production is in early development stages

and this research will assist the upgrade biofuel production from laboratory to commercial

production. This thesis makes an important contribution to national or international

investors by providing accurate geographic land suitability location maps. Furthermore it

has provided valuable information for local communities and governments in order to

consider crop combination or land use change as an alternative for eliminating

environmental issues.


70

4.3 Limitations and future research

The main limitation of the study was the lack of historical, economic and land use

data. To overcome these restriction, I adopted ArcGIS to interpolate the missing data

locations. The main benefit of this approach is that it enabled analysis but it is not a

substitute for accurate long-term data.

Water resources location and nutrient source data were another limitation for this

study. More precise researches needs to be done with those updated data for locating

accurate locations for algae commercial farm.

Future work needs to focus on environmental and socio-economic effects of algae

production at commercial scale, as those were out of this study scope. To assess the

practicalities of such production in specific areas, further learnings from research in

smaller scale and details will be necessary. These studies would be included more

economical factors from crop cultivation to producing oil.

4.4 Conclusion

To optimise the benefits and constraints of particular land uses in a certain area, a

planner needs geographically detailed mapping of specific characteristics of related to

the purpose of land use as inputs to the planning process. In this study, the criteria

specifically related to algae production were firstly investigated and analysed. Then

using ArcGIS applications, locations in Queensland suitable for algae were categorised

as poor, moderate and good locations for biofuel production as the first objective of the

study. The map result of this part of study shows that Queensland has very favourable

condition and land for algae production at commercial scales. Highly suitable sites are

mostly located in North West and along East of Queensland. Those areas have the

advantages of low land value, non-agricultural land use coupled with proper climatic

condition.

Combing this map with the existing studies of potential sugarcane production from

Audit, CO2 resource map and road map showed the locations which are suitable for

combination crop production, which are located in four areas. The first area is chosen

according to its low land value and suitable eco-climatic and land use situation. The


71

other areas are chosen based on proximity to mills, CO2 resources and infrastructure.

It’s important to note, there is a concern for water accessibility of water and nutrition

of the suggested areas. Due to lack of data, some resources were not evaluated

(particularly co-produced water and agricultural wastewater). So, it’s not assured if those

areas are economically sustainable for cultivation algae in case of transport

requirement.

Queensland is large state with lots of resources and proper climate. Therefore,

future work could focus on a smaller geographic area to investigate the potential of algae

cultivation precisely. The authors believe that, the information provided in this study will

serve as a base for further studies of the algae biofuels potential in Queensland and

assists policymakers, industry developers and decision makers.


72

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